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Levine Phenotypic Age Spreadsheet

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Apologies if this has been posted before, my search didn't find anything. Below is the abstract from the paper summarizing certain identified biomarkers of aging.

An epigenetic biomarker of aging for lifespan and healthspan

"Identifying reliable biomarkers of aging is a major goal in geroscience. While the first generation of epigenetic biomarkers of aging were developed using chronological age as a surrogate for biological age, we hypothesized that incorporation of composite clinical measures of phenotypic age that capture differences in lifespan and healthspan may identify novel CpGs and facilitate the development of a more powerful epigenetic biomarker of aging. Using an innovative two-step process, we develop a new epigenetic biomarker of aging, DNAm PhenoAge, that strongly outperforms previous measures in regards to predictions for a variety of aging outcomes, including all-cause mortality, cancers, healthspan, physical functioning, and Alzheimer's disease. While this biomarker was developed using data from whole blood, it correlates strongly with age in every tissue and cell tested. Based on an in-depth transcriptional analysis in sorted cells, we find that increased epigenetic, relative to chronological age, is associated with increased activation of pro-inflammatory and interferon pathways, and decreased activation of transcriptional/translational machinery, DNA damage response, and mitochondrial signatures. Overall, this single epigenetic biomarker of aging is able to capture risks for an array of diverse outcomes across multiple tissues and cells, and provide insight into important pathways in aging." https://www.ncbi.nlm.nih.gov/pubmed/29676998

I ran across a discussion on the subject, which includes a spreadsheet linked in the first post which may be of interest to some here.

A Spreadsheet for Calculating Your Levine Phenotypic Age

 "An excellent paper by M. E. Levine, et al, entitled "An epigenetic biomarker of aging for lifespan and healthspan" describes a technique for combining nine blood-work values with calendar age to calculate your Mortality Score (probability of death in the next ten years) and your Phenotypic Age, i.e., your apparent biological age as implied by your blood variables.  The calculation procedure is rather arcane, involving non-obvious unit conversions, exponentials, and logarithms, so I have produced an Excel spreadsheet (LINK) for performing these calculations. ..."


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  • 2 weeks later...

Yeah, my MCV has gone up to just over 100, but it seems that if asymptomatic, elevated MCV is not an issue. You look younger than that 😄 😄

It looks like RDW has a large effect. Also CRP. I am 42.49 for phenotypic and 42.04 for DNAm age. I really wanted to be in my 30s.... 😄

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11 minutes ago, Ron Put said:

it seems that if asymptomatic, elevated MCV is not an issue.

Maybe... [1]


[1] J Am Geriatr Soc. 2013 Jan;61(1):84-9. doi: 10.1111/jgs.12066. Epub 2013 Jan 10.

Relationship between mean corpuscular volume and cognitive performance in older adults.

Gamaldo AA(1), Ferrucci L, Rifkind J, Longo DL, Zonderman AB. Author information: (1)Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, Maryland 21224, USA. Alyssa.Gamaldo@nih.gov Comment in J Am Geriatr Soc. 2013 Jan;61(1):155-7.

OBJECTIVES: To examine the relationship between erythrocyte mean corpuscular volume (MCV) and cognitive performance over time. DESIGN: Longitudinal. SETTING: Sample from the Baltimore Longitudinal Study of Aging (BLSA). PARTICIPANTS: Eight hundred twenty-seven participants from the BLSA (mean age 67, range 50-96).

MEASUREMENTS: Mean corpuscular volume and several other blood indices were measured, including hemoglobin, iron, ferritin, vitamin B12, folate, white blood cell count, albumin, and erythrocyte sedimentation rate. Cognitive performance was examined using neuropsychological measures of visual memory, verbal memory, language, attention, executive function, and global mental status.

RESULTS: High MCV levels were significantly associated with lower global mental status even after adjusting for potential confounders. High MCV levels were also significantly associated with high rates of decline on tasks of global mental status, long delay memory, and attention, even after adjusting for potential confounders.

CONCLUSION: The findings confirm a previous observation that larger erythrocytes in older adults are associated with poorer cognitive function. Anemia and inflammation do not appear to explain the relationship between MCV and cognition. Further research is needed to clarify the mechanisms behind this association.

© 2012, Copyright the Authors Journal compilation © 2012, The American Geriatrics Society. DOI: 10.1111/jgs.12066 PMCID: PMC3555566 PMID: 23301873 [Indexed for MEDLINE]

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I would say that a higher level is an overall advantage:

Hematological parameters and all-cause mortality: a prospective study of older people.
Frąckiewicz J, Włodarek D, Brzozowska A, Wierzbicka E, Słowińska MA, Wądołowska L, Kałuża J.
Aging Clin Exp Res. 2018 May;30(5):517-526. doi: 10.1007/s40520-017-0791-y. Epub 2017 Jun 29.
PMID: 28664457 Free PMC Article
The effect of low and high concentration of some hematological parameters in the blood can have a negative impact on health.
Therefore, we investigated the associations between hematological parameters and all-cause mortality among older people living in Poland.
The study was carried out among 75-80-year-old participants (n = 403) from Warsaw and Olsztyn regions, Poland. Information on lifestyle factors and food consumption were obtained at baseline (June 1, 1999) using a self-administered questionnaire. Red blood cell, haemoglobin, hematocrit, mean corpuscular volume (MCV), mean corpuscular haemoglobin (MCH), and mean corpuscular haemoglobin concentration (MCHC) were determined. The data on deaths from all-causes were collected from the baseline until October 31, 2006. During an average of 7.4 years of follow-up, we ascertained 154 cases of death from all-causes.
Compared with men in the lowest tertile of MCV, MCH, and MCHC, the multivariable hazard ratios (HRs) of all-cause mortality in those in the highest tertile were 0.35 (95% CI, 0.17-0.73), 0.32 (95% CI, 0.16-0.67), and 0.44 (95% CI, 0.22-0.88), respectively. In contrast, among women after combining the second and the third tertiles of MCV, MCH, and MCHC, the HRs were 2.01 (95% CI, 1.01-3.99), 1.71 (95% CI, 0.85-3.43), and 1.09 (95% CI, 0.62-1.94), respectively.
We observed inverse associations between some hematological parameters and all-cause mortality among men, but not among women. This may be explained by a difference in iron metabolism, iron status, hormone regulations, or the occurrence of some diseases.
Gender; Hematological parameters; Mortality; Older people; Prospective study

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Thanks Al. What seems strange is why, given the study you cite (in which higher MCV was associated with lower mortality, at least in men), the Levine Phenotypic Age equation suggests MCV is positively correlated with phenotypic age (i.e. higher MCV implies higher age).

Perhaps the Levine approach to estimating age from biomarkers isn't as good as Ron suggests.

And unfortunately, long lifespan is compatible with impaired cognitive performance, both of which may result from (or at least correlate with) elevated MCV. 


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1 hour ago, Dean Pomerleau said:


Perhaps the Levine approach to estimating age from biomarkers isn't as good as Ron suggests.


Well, if one does a little research, its predictive abilities are rather good and better than most. It also jives with other research on the subject (e.g., https://onlinelibrary.wiley.com/doi/full/10.1111/acel.12557 )

As to increased MCV values, as I noted above, the key is being asymptomatic (although I ran into a study I'll cite below which seems to indicate otherwise). For instance, during fasting, MCV values generally increase, as folate, B-12 and iron are depleted. Chronic deficiencies in those would also trigger an increase in MCV. Rapid turnover of RBCs will also do it. RBCs also depend on glucose for survival, so very low glucose levels would impact them as well (not so in my case). Or B malabsorbtion. In my case, I was thinking of double checking my B-12, since my folate appears fine.

But it also appears rather well documented that increased MCV are predictive of mortality in many clinical instances. Here are a couple of examples:

The prognostic value of interaction between mean corpuscular volume and red cell distribution width in mortality in chronic kidney disease



Mean corpuscular volume levels and all-cause and liver cancer mortality

"Elevated MCV level was related to an increased risk of liver cancer mortality in men (aHR, 3.55; 95% CI, 1.75-7.21). Conclusions: This study suggests that the elevated MCV level in non-anemic cancer-free individuals was associated with increased all-cause mortality in both men and women, and with cancer mortality, in particular liver cancer mortality in men."



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So perhaps, like it does with testosterone, CR push MCV in a direction that is associated with negative health outcomes in the "normal" population.  For example, perhaps elevated MCV is a early biomarker of certain cancers. But when elevated due to benign causes like CR or fasting, elevated MCV might not be predictive of bad outcomes - or at least one can hope.


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  • 8 months later...

That was fairly fun to complete - I had 8/10 values available, so I left the c-reactive protein and red cell dist width as is from the previous user. Apparently I come out at about 10 years younger than my chronological age. Looks like I'm also in the category of having a higher MCV. 


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21 hours ago, drewab said:

That was fairly fun to complete - I had 8/10 values available, so I left the c-reactive protein and red cell dist width as is from the previous user. ...

You are relatively young, so it's likely that your Red Cell Dist Width is closer to 12.5 or even lower. It makes a significant difference.  It's a calculated number you generally have to ask specifically for, as most labs don't include it in reports, even though it's available from a standard blood panel.

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  • 8 months later...

Sorry for a bit of threadomancy here, but I found this as I was researching different aging biomarkers to track (https://www.longevityadvice.com/aging-biomarkers/) and I'm curious if people have kept up their tracking of this biological age calculation? It seems ideal since most markers can be got from simple blood tests, and while not *as* correlated as Horvath's clock, appears more accessible to the average joe.

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  • 1 year later...

I started to consult the available material on epigenetic clocks. Very interesting, but lots of caveats, especially so if you watch the interview with Steve Horvath and Rhonda Patrick.

I'm in the process to watch the interview with Morgan Levine and Rhonda Patrick. The concept is fascinating, with the methylation groups on CpGs loci acting as switches or maybe dimmers which regulate the genetic expression of the selected genes.

The final result of LEvine's clock is the result of a multiple regression on data drawn from an observational study though, with all the drawbacks of a statistical method.

My main doubt is the absence of an error bar in these methods. Horvath speaks explicitly about the error bar which should be declared in the Grimage clock for example.

With an error bar, we may find that at the lower bound of the range we could have the age of a foetus.

The above are just first impressions, I have to dive deeper into the subject.

My feelings about the error: DNAmpheno age and chronological age in blood and saliva samples, the data scatter is absolutely nontrivial


Edited by mccoy
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NOTE: PhenoAge just based on blood markers is NOT the same as the DNAm metric based(?) on phenoage.

It also doesn't contain homocysteine (though homocysteine can be reduced to *some* extent). The biggest coefficients of PhenoAge come from CRP and RDW.

Edited by InquilineKea
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41 minutes ago, InquilineKea said:

The biggest coefficients of PhenoAge come from CRP and RDW.

Inquiline, That's another aspect of interest. The sensitivities of the single variables. If the effect of CRP is strong, then inflammation is one of the key states to avoid, obviously but with a priority. The effects of RWD, the variability in size of red blood cells, are less clear, I don't know if the favourable state is limited variability or greater variability and which are the factors influencing such variability, the effects being a variation in oxygen transportation apparently.

One variable that I hate in Levine's clock is creatinine. I take creatine, so the value of blood creatinine will sure turn out very high, compromising the reliability of the clock. Since many fitness buffs take creatine, Levine should substitute that variable with another chosen among the remaining 33.

Listening to the talks of Horvath and Levine also gives rise to some tiny confusion. They agree that the methylome is influenced by lifestyle, although the signal is weak. The field is actively searching compounds, in vitro and in vivo, which are able to send stronger signals (actuating the right switches for longevity). It also is not very clear if the methylome has a distinct causal role in slowing or accelerating age. 

In the words of Horvath, clocks like the grimage clock have a value only if the phenotypic age is in the upper or lower percentiles, then the slowing or accelerating in aging can be considered a real thing.

The field is still very young and many researchers are active. Also, there are huge economical interests. Horvath and Levine speak freely of their research but if the epigenetic model really turns out to be a main regulation of the aging clock, with a causal role, then many breakthroughs are possible and many researchers in private companies keep the results to themselves.

One of such breakthroughs is probably the possibility to test the effects of molecules on the epigenetic clock. The incredible advantage of such tests is that they are tests on aging which require relatively very little time (and probably effort).

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Guys (and girls)....


I don't think we can take any of these "biological age"-estimators too serious at this point in time. They are not validated in prospective studies. It's just a retroactive optimization of parameters on some regression analysis. It's commonly done in economics (called "calibration") and to a mathematically more complex extend in astrophysics - and it's unclear what value you can get out of it without doing the experiment.


That means: make a prediction on a variety of datasets for which your model isn't already optimized for as a best fit.

In aging biology that would mean ideally taking a group of already quite old individuals (above age 80) without preexisting serious conditions and collecting data and mortality rates for at least 5 years of follow up.

It still doesn't quite settle the issue of their real world predictive value - modern medicine and generational differences in environmental exposure might make some bio markers invalid for younger generations - but at least gives empirical validity beyond optimizing for a best fit on existing datasets.


Calibration on general equilibrium models in economics is mostly highly questionable by the way (as are the optimization games in computational astrophysics).



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"They are not validated in prospective studies"

Not true.

Older ages on Levine's test, aging.ai, Horvath, Hannum and others are associated with an increased all-cause mortality risk.

Now, if you said, they're not validated in RCTs, i.e. reduce biological age--> reduced all-cause mortality risk, then that's mostly true, except for DunedinPACE, which is the only epigenetic test to detect a younger biological age as a result of people being on CR for 2 years.

The absence of evidence is because those studies haven't been performed, rather than performed and no effect was identified.

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They are associated with all-cause mortality on the datasets that they are optimized for, yes. But that's hardly a surprise.

The question of validation is:

1. Are they at least validated against datasets which they are not already optimized for? If this is already exiting datasets this would ideally be on a different population with different environmental or genetic background (to figure out, if they are truly capturing aging). Do we have that data?

2. Most ideally it would be prospective cohort study designed from the outset to validate their "clocks"; i.e. select a sample of older people; rule out preexisting conditions; normalize for lifestyle. Make a prediction based on initial biomarkers. Follow up for 5 years; compare with initial prediction - and maybe collect data every year to evaluate changes in biomarkers.


Validating them against RCTs for "anti-aging" interventions is a challenge, as we don't truly know if a given intervention is delaying or even reversing aging in humans. Is CRON working in humans? We don't really know at the moment. Rapamycin might work - but the dosing regimen of people doing it for anti-aging is so ridiculously lower than the equivalent dosing in ITP-mice that it's again unclear if it works. You could argue, that statins are delaying aging of the cardiovascular system in humans (and even mortality) - but by how much does this "isolated anti-aging effect" impact overall aging?

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Guest, IMO, your skepticism about "expert's" anti-aging regimens is 100% justified.  (However, I think your skepticism about CRON may be overdone -- we do know, from Luigi's studies, that people on serious CRON show signs of aging more slowly than the general population.  That hardly proves the likelihood of a longer healthspan and lifespan for CRONnies, but it does suggest it.)

To get absolute proof, we have to wait for people to start dying.


  ---  Saul

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18 hours ago, Saul said:

Guest, IMO, your skepticism about "expert's" anti-aging regimens is 100% justified.  (However, I think your skepticism about CRON may be overdone -- we do know, from Luigi's studies, that people on serious CRON show signs of aging more slowly than the general population.  That hardly proves the likelihood of a longer healthspan and lifespan for CRONnies, but it does suggest it.)

CRON is one of those interventions which as Saul rightly underlines, suggests a slowing of aging. Others cited by Horvath and Levine are all those which we already know: not eating meat, eating abundant vegetables, doing exercise, being female, not smoking, not being obese, not having diabetes, also not having too low income (not often cited).

The real word is different from the mathematical model as guest hinted at and it seems that the researchers are searching for validations. Some of em in vitro, others in vivo in lab animals, others in humans on a shorter timescale


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This is an excellent TEDX presentation by Horvath himself, in 2020

Some interesting remarks:

The GrimAge clock (mortality predictor) predicts an HR of 2 at the top 5% of its value, conversely a HR of 0.5 in the bottom 5%. This is a statistical information which has a certain value. The mathematical model predicts double the mortality ratio in those people who have the uppermost values >=95 %-ile of the GrimAge clock. an hazard Ratio of 2 starts suggest a significant correlation between GrimAge and death and when the correlation is significant the probability of causation starts to become strong.

He also presented a plot of an in vitro experimenti with human cell cultured with Rapamycin and another molecule, plus a mixture of the two. Rapa is associated with a distinct slowing of age.




He also projects a plot of a phase I clinical trial on humans which exhibited a 2.5 years age reversal in 12 months. I'm curious to see the developments of this trial, led by Greg Fahy, who by the way describes it in another presentation. 




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