Biological age testing has become much more prevalent over the past few years. This is due to the longevity movement which has helped educate physicians and patients that biological aging is the number one risk factor for almost every chronic disease and is closely related to quality of life as we age.
However, as many testing companies rush to fill this void, it’s important to know how to evaluate the available tests in order to discern which is best for you to use and what it can – and cannot – tell us.
In this review, we will look into factors you should consider when evaluating biological age tests, and offer our perspective on what makes for the best epigenetic and biological age tests. Of course, we encourage you to do your own research beyond this review of epigenetic clocks.
NOVOS Age vs. Other Biological Age Clocks
| Attribute | NOVOS Age Clock | Saliva-based Clock by Celebrity Scientist's New Startup | Other tests |
|---|---|---|---|
| Tissue Collection | Blood from a small poke of a finger, a method that is more accurate than via saliva | Saliva from a cheek swab, a method that is generally not very accurate | Blood collection methods that are invasive and far more uncomfortable that via small pokes of fingers |
| Sample Size | Samples from more than 20,000 humans | Samples from more than 8,000 humans | Samples typically from fewer than 2,500 humans |
| DNA Methylation Technology | Built using the modern MethylationEPIC array that measures 850,000 DNA sites and tests your sample on that same technology | Built using the modern MethylationEPIC array that measures 850,000 DNA sites but does not test your sample on that same technology | Built using older arrays that only capture 27,000-450,000 DNA sites |
| Chronological Age Range | 8-102 years | 18-100 years | Less expansive age range often lacking individuals 90+ years of age |
| Diversity | Significant diversity across ethnicity, race, and sex, all supported by many peer reviewed publications | Diversity across ethnicity, race, and sex, but without support of peer reviewed publications | Insufficient representation across ethnicity, race, and sex and without support of peer reviewed publications |
| Test Reliability | Optimized to be reliable across repeat measurements, with published and peer reviewed best-in-class ICC values (accuracy) >.96 for all three algorithms | Claims of being optimized to be reliable across repeat measurements without disclosing ICC values | Exhibit high test-retest error rates |
| Model Type | 3rd generation (latest) clock, the only one trained on longitudinal analysis (people across their lifetimes), the best way to track biological age as shown in publications and tested via peer review | Self-claimed "novel" method-based model that lacks publications, peer review, and head-to-head comparisons against other clocks | 1st generation (oldest) model trained to simply estimate chronological age instead of biological aging |
| Outputs and Analysis | Three: 1) 3rd generation Pace of Aging via DunedinPACE, 2) Biological Age, and 3) Telomere Length | One: A less accurate output of biological age | A single, less accurate output of biological age |
| Creators of Clock | A top team of Duke University and Columbia University scientists with peer reviewed publications | A start up company without publication of the algorithms, thus lacking scientific scrutiny | |
| Number of Studies | 45+ published studies by 30+ longevity scientists' labs across the world | Zero published studies | |
| Immune Cell Controls | Published and Patented Advanced 12-cell immune deconvolution methods (cell changes won't impact accuracy, which is common in saliva and makes blood samples better) | No controls | No controls |
| Studies that prove accuracy in different ethnic groups | Algorithms validated in the Family and Community Health Study of African American Families study, MESA (Multi-ethnic Study of Atherosclerosis), Cebu Longitudinal Health and Nutrition Survey (CLHNS Phillipines), Northern Finland Birth Cohort 1966 Study, Health and Retirement Study, the Normative Aging Study, the Framington Heart cohort, TILDA (the Irish Longitudinal Study of Aging), and many more. | No studies | No studies |
| Studies that show relatonship to outcomes | Algorithms have been validated in the Health and Retirement Study, the Normative Aging Study, the Framington Heart cohort and more. | No studies | No studies |
| Studies that show change with validated anti-aging interventions | The only algorithm proven to respond in a significant way to validated anti-aging interventions such as caloric restriction (Published in Nature) | No studies | No studies |
| Include Clinical Covariates | 21 clinical covariates and telomere length | No clinical covariates | No clinical covariates |
| Comparisons to other algorithms | Comparisons in the FHS study and in the Health and Retirement Study show superior results | No published comparisons | No published comparisons |
| Shares actual data on precision (ICC values) | See ICC values with comparisons in the FHS study. | No data | No data |
Considerations to make when evaluating epigenetic and biological age tests
Here are the top factors to keep in mind when determining if an epigenetic biological test kit is appropriate for your needs:
- Are they published? Many of the most popular commercial biological age tests have never been published for scientific review, including even those launched by big name scientists or people who claim to have affiliations with academic labs. Using their tests is somewhat like going to a fortune teller: you can choose to believe them, but it’s better if they proved that their algorithms work.
Since there is no transparency about these algorithms, we neither know how they relate to diseases and outcomes nor their accuracy, which are ultimately the values that biological age clocks provide.
Further, we have no idea how precise these clocks are, so we can’t confidently form any conclusions as to how much change is considered to be biologically significant.
When you consider a biological age test, check to see if the algorithms are published for scientific evaluation. For example, NOVOS Age uses the DunedinPACE clock, developed by Columbia University and Duke University researchers, which as of February 2023 has 45 scientific papers published and is widely considered to be the most powerful epigenetic test to date.
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- Do they use saliva or blood? Although saliva is easier to submit a sample for, there are a number of shortcomings when using that method rather than blood, which we will cover more in this article. NOVOS Age uses blood, which yields far more accurate results, which is of utmost importance when selecting a biological age test.
- Do they control for cell types found in the sample? Be wary of claims made by companies who sell biological age tests that don’t control for cell types found in the sample.
If the test does not control for cell types found in the sample, it means that there can be large variations for a single person throughout the course of the day. For example, epithelial and lymphocyte cells can change with smoking, food and drink, diseases, and time of day. Not controlling for cell types in the sample means that they don’t control for material fluctuations.
- Do they train their clocks on chronological age or biological age (disease and mortality)? Chronological age (how many birthdays you’ve had) is what first generation clocks (launched in 2011) were trained on and have since been determined by scientists to be flawed (we’re now on to third generation clocks, with fourth generation clocks coming in the future).
We don’t care for clocks trained on chronological age because we all already know how old we are in the traditional sense. In fact, it’s been found that chronologically trained clocks give incorrect outcomes (show an older age) for interventions that we know extend lifespan (e.g., fasting).
What we want to know is how biologically old or young we are. When considering a biological age test, make sure to ask if the algorithms have been trained on biological age markers such as mortality, diseases, and physiological markers of age. NOVOS Age’s algorithm is trained on mortality rates, disease rates, more than a dozen blood biomarkers, lifestyle surveys, and more than two dozen other factors associated with aging (e.g., white matter in the brain, grip strength, 1-second forced expiratory volume, etc.).
Is the biological age test published and validated?
NOVOS Age only uses published algorithms, which is important since they have been validated by scientists to be successful measurements of what they are claiming to measure. Without published, peer reviewed data, there is no way to maintain the integrity of the science, meaning that the validity, quality, and originality have not been confirmed. It is always better to have evidence that the testing is measuring what it claims to be.
In addition, this published data helps you learn more. As more research is published about these algorithms, we are able to find additional connections and relevance to health.
For example, with interventional trials, we are also able to find out which therapies, treatments, and lifestyle modifications are most helpful to impact these algorithms’ outputs and improve our health.
Not only are the algorithms used in NOVOS Age published, we have publications that compare our algorithms to other published tests. This way, you can see how they match up.
Additionally, we provide you with thorough, actionable guidance on improving your biological age scores based on scientific research, both in our NOVOS Age report and through our longevity blog.
NOVOS Age’s DunedinPACE algorithm is published and we know how it predicts outcomes
Values you should consider looking at when evaluating epigenetic biological age tests are the hazard ratios to disease per algorithm. In studies of interventions, the hazard ratio is an estimate of the ratio of the hazard rate (risk of disease) in the treated versus the control group.
For instance, if someone is 5 years older biologically, how do we know their risk of death? How would we know their increased or decreased risk of cardiovascular disease or dementia? We do this calculation with a hazard ratio. Here, 1 is usually the control, and the larger the number is above 1, the more likely the event is to occur.
You can see the table below as an example. For instance, for every standard deviation in NOVOS Age’s DunedinPACE, you would see a 64% increase (hazard ratio of 1.64) in the risk of death. One standard deviation of the first generation 2013 horvath algorithm would only represent a 2% increase in death.
As the table shows, DunedinPACE outperforms every algorithm. The exception is GrimAge, but that is only because this validation dataset was the same data used to train GrimAge and thus it is artificially elevated.
How is the biological age test related to quality of life metrics?
Living longer and disease free are major focuses of biological age tests. However, we all know how important quality of life is. So, ask yourself how the algorithm you are using is connected to quality of life. You can see below some images which compare published algorithms. Once again, NOVOS Age’s DunedinPACE continues to show large associations to multiple measures of quality of life.
How do other factors impact the algorithm? Make sure the testing company knows!
Wondering how race, ethnicity, sex, education, smoking, drinking or weight impact these metrics? With NOVOS Age, you can look at published studies, such as the excerpt below, to find associations. Does the other biological age test you’re considering have this information and make it public? We urge skepticism if they don’t.
Do they control for cell types in your sample? If not, their precision is likely flawed.
While epigenetics are extremely exciting as a biomarker, it can often be difficult to interpret as every cell has a different epigenetic signature. For instance, if we measured brain tissue with biological age algorithms, we would get lower ages than if we tested blood. If we tested breast tissue, we would get higher ages than if we tested blood. This is because the epigenetic methylation signature is different across tissues. This can impact algorithms in ways that lead to inaccuracies.
That is why we at NOVOS only use blood with NOVOS Age. Blood is a tissue type which has control features such as cell deconvolution methods. This allows us to know what cell types are present at what percentages so we can make sure that tissue type can be controlled for properly. If an algorithm was trained in blood, you wouldn’t want to measure it with saliva. This is because saliva can include epithelial cells, which are not found in blood.
We control for immune cell subsets with immune deconvolution methods. This is important for precision as variation of immune tissues needs to be taken into account. It has recently become a point of emphasis across the scientific community. Dr. Eric Verdin of the Buck institute discussed it recently.
Controlling for cell types is incredibly important to make sure you are seeing real aging signals and not just changes in the cell type you are testing. NOVOS Age uses advanced 12 cell immune deconvolution methods.
What are their ICC values?
When it comes to biological age testing, there are specific metrics you should look at when evaluating an algorithm. The first in the intraclass correlation (ICC) value. This is how testing precision is measured. This is a statistical number which describes how a number within a group compares to each other.
For instance, if we take blood from an individual and split it into five samples that are each measured separately, we would expect that all of the results are the exact same as each other. Unfortunately, with lab testing, this isn’t always the case as sometimes these results can vary despite coming from the exact same sample.
For precise tests, this variation is usually small. This would usually represent an ICC value of 0.9 or greater. An ICC value of 1.0 would mean perfect agreement. 0.5 would mean poor agreement. Thus, you should always look at the algorithm’s ICC value, especially because this has been an issue with previous clocks.
NOVOS Age’s algorithm has the highest precision of any algorithm, at a hair below 1.0 (see chart below).
Is their scale limited? Is it compatible with updated insights and information?
When we test your epigenome, we are looking at areas called CpGs, which can be methylated (i.e., turning a gene off; essentially, the process of epigenetics). There are over 28 million different CpG locations in each cell. This is great because it gives us the ability to create very precise algorithms. However, most people would not test all of these due to high costs.
At NOVOS, we have algorithms created from approximately 900,000 CpGs. We also use the same testing infrastructure as almost all of the clinical researchers. We do this because it is the best value for the money. That is, the more data we collect, the more we are able to report back to you. It also allows us to collaborate with researchers to find additional insights into the aging process.
If we were only able to measure 100,000-200,000 CpGs, it would significantly limit the types of insights we’d be able to provide you with from this data. For instance, as the more precise algorithms were created, we implemented them immediately into our population to provide the most accurate results. Find out how many CpGs the biological age test you are considering tests for.
Are they using the latest generation clocks?
The problem with first generation clocks is that they were trained on chronological age – something we already know and outside of research settings has no practical value. What we really care about is the biochemistry of aging, or your biological age. So, how can we detect that better?
The answer is to measure and train these DNA methylation patterns to better measurements of aging than chronological age and this is what the second generation clocks did. The three most popular second generation clocks are 1) PhenoAge (Levine et al., 2018), which was trained to 10 blood measurements; 2) GrimAge (Lu et al., 2019), which was trained to predict 12 protein measurements and time until death; and 3) Telomere Length Clock (Lu et al., 2019), which was trained to predict telomere length.
These second generation clocks were much better than the first generation clocks. How do we know? Accelerated aging scores were more predictive of negative health outcomes, and decelerated aging scores with more positive health outcomes (Bergsma and Rogaeva 2020).
Beyond this, the second generation clocks were also more associated with diseases (Bergsma and Rogaeva 2020). Even then, there was still room for improvement. This is because these second generation clocks were created with samples from many people over different timepoints in their life. To get the best aging signal, it would be best to follow the same individuals across their life at various time points – a longitudinal study.
That’s exactly what the NOVOS Age DunedinPACE clock did. Unlike previous clocks, the Dunedin Pace of Aging (DunedinPACE) was not trained on chronological age, and instead it’s the first clock to be trained entirely on phenotypes of aging in the same patients across their lifespan – all the way from age 3 to age 51. This is helpful because we aren’t picking up “noise” in our measurements. By following the same individuals we can make sure that things like environmental exposures aren’t included in these clocks.
For example, 50 years ago many people were exposed to more lead through leaded gasoline, less antibiotics, and less microplastics. If we don’t control for the time at which people lived, our algorithm might include markers associated with these exposures rather than just measuring aging.
Generally, the more biologically informed an algorithm is, the better it is at capturing the signal of biological aging and reducing other confounding factors.
When you’re considering biological age tests, ask whether the test is third generation. That is, ask if it’s trained on longitudinal data.
Do the algorithm’s results change if you improve your aging?
Biological age clocks are the best ways to predict age related outcomes. However, we still don’t know exactly why we see these patterns that emerge in our DNA.
In order to make sure that the measurement is reliable and useful, we also need to make sure that the clocks respond to interventions we already know beneficially affect biology and aging. An article by Jamie Justice, PhD, from Wake Forest, outlines the following criteria for an aging biomarker.
As you can see, at the time of publication in 2020, none of the clocks had been able to fulfill this last criteria. However, this has changed. Now, the DunedinPace clock available in NOVOS Age has satisfied all criteria.
One of the cohorts used to validate this consisted of middle-aged, non-obese adults enrolled in the CALERIE trial. This trial tested the effects of caloric restriction – an intervention that has been successful in a variety of studies to improve biological aging – over a period of two years.
As you can see in the image below, just as expected, the NOVOS Age DunedinPACE was able to show a decrease in the rate of aging in those groups who restricted calories by approximately 11% over two years.
However, the importance of this goes beyond validation of NOVOS Age’s DunedinPACE epigenetic clock. This data also shows that the first generation algorithms’ age output actually increased with caloric restriction. This shouldn’t happen as all other types of aging markers in the study improved. Thus, this goes to show that first generation algorithms don’t always respond to interventions correctly – all the more reason to insist on a third generation algorithm
What Are The Best Biological Age Tests?
When shopping for a biological age test, it’s important that you’re informed on the science behind these innovative tests before making a decision.
After consulting with many experts in the field for more than 18 months and evaluating all of the biological clocks, we feel confident that the DunedinPACE clock contained in NOVOS Age is the most powerful, precise, and actionable of all clocks out there – but we leave the choice to you!
Additional Resources
What Are Epigenetic Clocks And How Can They Determine My Real Age?
What Are The Best Epigenetic Clocks?
How To Slow Down Or Even Turn Back The Epigenetic Aging Clock
| No. | Publication Year | Researchers | Study | Data Set |
|---|---|---|---|---|
| 1 | 2023 | Yu, Y.-L. | Current Marital Status and Epigenetic Clocks Among Older Adults in the United States: Evidence From the Health and Retirement Study. Journal of Aging and Health, 35(1–2), 71–82. https://doi.org/10.1177/08982643221104928 | The US Health and Retirement Study |
| 2 | 2023 | Caro, Juan Carlos, Cyrielle Holuka, Giorgia Menta, Jonathan D. Turner, Claus Vögele, Conchita D’Ambrosio, | Children’s internalizing behavior development is heterogeneously associated with the pace of epigenetic aging, Biological Psychology, Volume 176, , 108463, https://doi.org/10.1016/j.biopsycho.2022.108463. | |
| 3 | 2023 | R Waziry, DL Corcoran, KM Huffman, MS Kobor, M Kothari, VB Kraus, WE Kraus, DTS Lin, CF Pieper, ME Ramaker, M Bhapkar, SK Das, L Ferrucci, WJ Hastings, M Kebbe, DC Parker, SB Racette, I Shalev, B Schilling, DW Belsky | Effect of Long-Term Caloric Restriction on DNA Methylation Measures of Biological Aging in Healthy Adults: CALERIE™ Trial Analysis. medRxiv 2021.09.21.21263912; doi: https://doi.org/10.1101/2021.09.21.21263912 | CALERIE Trial |
| 4 | 2022 | Raffington, L., Tanksley, P. T., Sabhlok, A., Vinnik, L., Mallard, T., King, L. S., Goosby, B., Harden, K. P., & Tucker-Drob, E. M. | Socially Stratified Epigenetic Profiles Are Associated With Cognitive Functioning in Children and Adolescents. Psychological Science, 0(0). https://doi.org/10.1177/09567976221122760 | Texas Twin Study |
| 5 | 2022 | Etzel, L, et al. Shalev, I. | Obesity and accelerated epigenetic aging in a high-risk cohort of children. Scientific Reports. 12, | The Child Health Study |
| 6 | 2022 | Kuzawa CW, Ryan CP, Adair LS, Lee NR, Carba DB, MacIsaac JL, Dever K, Atashzay P, Kobor MS, McDade TW. | Birth weight and maternal energy status during pregnancy as predictors of epigenetic age acceleration in young adults from metropolitan Cebu, Philippines. Epigenetics. 2022 Nov;17(11):1535-1545. doi: 10.1080/15592294.2022.2070105 | Cebu Longitudinal Health and Nutrition Survey (CLHNS Phillipines) |
| 7 | 2022 | Freni-Sterrantino A, Fiorito G, D'Errico A, Robinson O, Virtanen M, Ala-Mursula L, Järvelin MR, Ronkainen J, Vineis P. | Work-related stress and well-being in association with epigenetic age acceleration: A Northern Finland Birth Cohort 1966 Study. Aging (Albany NY). Feb 2;14(3):1128-1156. doi: 10.18632/aging.203872. | Northern Finland Birth Cohort 1966 Study |
| 8 | 2022 | Botong Shen, Ph.D.1, Nicolle A. Mode, M.S.1, Nicole Noren Hooten, Ph.D.1, Natasha L. Pacheco Ph.D.,1, Ngozi Ezike, M.D.1, Alan B. Zonderman, Ph.D.1, and Michele K. Evans, M.D. | Association of race and poverty status with a faster pace of biological aging. | The HANDLE Study (NIA intramural study of older adults) |
| 9 | 2022 | Kim Y, Huan T, Joehanes R, McKeown NM, Horvath S, Levy D, Ma J. | Higher diet quality relates to decelerated epigenetic aging. Am J Clin Nutr;115(1):163-170. doi: 10.1093/ajcn/nqab201. | The Framingham Offspring Study. |
| 10 | 2022 | Simons, Ronald L., Mei Ling Ong, Man-Kit Lei, Eric Klopach, Mark Berg, Yue Zhang, Robert Philibert, Frederick X. Gibbons, Steven R.H. Beach | Shifts in lifestyle and socioeconomic circumstances predict change—for better or worse—in speed of epigenetic aging: A study of middle-aged black https://doi.org/10.1016/j.socscimed.2022.115175, Social Science & Medicine, Volume 307, 2022, 115175, | The FACHS Study |
| 11 | 2022 | Beach, S.R.H.; Klopack, E.T.; Carter, S.E.; Philibert, R.A.; Simons, R.L.; Gibbons, F.X.; Ong, M.L.; Gerrard, M.; Lei, M.-K. | Do Loneliness and Per Capita Income Combine to Increase the Pace of Biological Aging for Black Adults across Late Middle Age? Int. J. Environ. Res. Public Health 2022, 19, 13421. https://doi.org/10.3390/ijerph192013421 | The FACHS Study |
| 12 | 2022 | Lei MK, Berg MT, Simons RL, Beach SRH. | Neighborhood structural disadvantage and biological aging in a sample of Black middle age and young adults. Soc Sci Med. | The FACHS Study |
| 13 | 2022 | Avila-Rieger, Justina, Indira C. Turney, Jet M.J. Vonk, Precious Esie, Dominika Seblova, Vanessa R. Weir, Daniel W. Belsky, Jennifer J. Manly | Socioeconomic Status, Biological Aging, and Memory in a Diverse National Sample of Older US Men and Women. Neurology 99 (19) e2114-e2124; DOI: 10.1212/WNL.0000000000201032. | Health and Retirement Study |
| 14 | 2022 | Graf Gloria H, Christopher L Crowe, Meeraj Kothari, Dayoon Kwon, Jennifer J Manly, Indira C Turney, Linda Valeri, Daniel W Belsky. | Testing Black-White Disparities in Biological Aging Among Older Adults in the United States: Analysis of DNA-Methylation and Blood-Chemistry Methods, American Journal of Epidemiology, Volume 191, Issue 4, April 2022, Pages 613–625, https://doi.org/10.1093/aje/kwab281 | Health and Retirement Study |
| 15 | 2022 | Graf Gloria Huei-Jong, Yalu Zhang, Benjamin W Domingue, Kathleen Mullan Harris, Meeraj Kothari, Dayoon Kwon, Peter Muennig, Daniel W Belsky | Social mobility and biological aging among older adults in the United States, PNAS Nexus, Volume 1, Issue 2, pgac029, https://doi.org/10.1093/pnasnexus/pgac029. | Health and Retirement Study |
| 16 | 2022 | Kelly E. Rentscher, Eric T. Klopack, Eileen M. Crimmins, Teresa E. Seeman, Steve W. Cole, Judith E. Carroll | Lower Social Support is Associated with Accelerated Epigenetic Aging: Results from the Health and Retirement Study. medRxiv 2022.06.03.22275977; doi: https://doi.org/10.1101/2022.06.03.22275977. | Health and Retirement Study |
| 17 | 2022 | Schmitz, Lauren L. & Duque, Valentina | In-utero exposure to the Great Depression is reflected in late-life epigenetic aging signatures. PNAS, 119 (46) e2208530119. https://doi.org/10.1073/pnas.2208530119. | Health and Retirement Study |
| 18 | 2022 | Peterson, Mark D. Stacey Collins, Helen C.S. Meier, Alexander Brahmsteadt, Jessica D. Faul | Grip strength is inversely associated with DNA methylation age acceleration. J of Cachexia, sarcopenia, and muscle. https://doi.org/10.1002/jcsm.13110. | Health and Retirement Study |
| 19 | 2022 | Schmitz LL, Zhao W, Ratliff SM, Goodwin J, Miao J, Lu Q, Guo X, Taylor KD, Ding J, Liu Y, Levine M, Smith JA. | The Socioeconomic Gradient in Epigenetic Ageing Clocks: Evidence from the Multi-Ethnic Study of Atherosclerosis and the Health and Retirement Study. Epigenetics. 2022 Jun;17(6):589-611. doi: 10.1080/15592294.2021.1939479 | MESA (Multi-ethnic Study of Atherosclerosis) and the Health and Retirement Study |
| 20 | 2022 | Reed RG, Carroll JE, Marsland AL, Manuck SB. | DNA methylation-based measures of biological aging and cognitive decline over 16-years: preliminary longitudinal findings in midlife. Aging; 14:9423-9444. https://doi.org/10.18632/aging.204376. | AHAB Study (Adult Health and Behavior Pittsburgh) |
| 21 | 2022 | McCrory C, Fiorito G, O'Halloran AM, Polidoro S, Vineis P, Kenny RA. | Early life adversity and age acceleration at mid-life and older ages indexed using the next-generation GrimAge and Pace of Aging epigenetic clocks. Psychoneuroendocrinology. Mar;137:105643. doi: 10.1016/j.psyneuen.2021.105643. . | TILDA (the Irish Longitudinal Study of Aging) |
| 22 | 2022 | Jesse R. Poganik, Bohan Zhang, Gurpreet S. Baht, Csaba Kerepesi, Sun Hee Yim, Ake T. Lu, Amin Haghani, Tong Gong, Anna M. Hedman, Ellika Andolf, Göran Pershagen, Catarina Almqvist, James P. White, Steve Horvath, Vadim N. Gladyshev. | Biological age is increased by stress, and restored upon recovery from stress, BioRxiv. | Three Clinical Datasets |
| 23 | 2022 | Michael Safaee, Varun Dwaraka, Justin K. Scheer, Marissa Fury, Tavis Mendez, Ryan Smith, Jue Lin, Dana Smith, Christopher P. Ames, | Cellular aging for risk stratification in adult deformity surgery: utilization of seven epigenetic clocks and two telomere length measurements in the analysis of comorbidity burden, frailty, disability and complications in adult deformity surgery, The Spine Journal, Volume 22, Issue 9, Supplement, 2022, Pages S13-S14, ISSN 1529-9430, https://doi.org/10.1016/j.spinee.2022.06.041. | Clinical sample. |
| 24 | 2022 | Sugden, K., A. Caspi, ML Elliott, KJ Bourassa, K Chamarti, DL Corcoran, AR Hariri, RM Houts, Meeraj Kothari, S Kritchevsky, GA Kuchel, J Mill. BS Williams, DW Belsky, TE Moffitt | Association of Pace of Aging measured by blood-based DNA methylation with age related cognitive decline and dementia. Neurology | ADNI Study (Alzheimers Disease Neuroimaging Initiative), Framingham Offspring Study |
| 25 | 2022 | Sugden, Karen, Terrie E. Moffitt, Thalida Em Arpawong, Daniel W. Belsky, David L. Corcoran, Eileen M. Crimmins, Eilis Hannon, Renate Houts, Jonathan S. Mill, Richie Poulton, Sandyha Ramrakha, Jasmin Wertz, Benjamin S. Williams, Avshalom Caspi | Cross-national and cross-generational evidence that educational attainment may slow the pace of aging. J of Gerontology | Health and Retirement Study, Generation Scotland, E-Risk Study, UK Understanding Society, Dunedin Study |
| 26 | 2022 | Belsky, DW, A Caspi, D Corcoran, K Sugden, R Poulton, L Arseneault, A Baccarelli, K Chamarti, X Goa, E Hannon,, HL Harrington, R Houts, M Kotharti, D Kwon, J Mill, J Schwartz, P Vokonas, C Wang, B Williams, TE Moffitt | DunedinPACE, A DNA methylation biomarker of the Pace of Aging, eLife. | Dunedin Study, Normative Aging Study, E-Risk Study, Framingham Heart Study, UK Understanding Society |
| 27 | 2022 | Ruiz, Begoña, Jonathan M. Broadbent, W. Murray Thomson, Sandhya Ramrakha, Terrie E. Moffitt, Avshalom Caspi, Richie Poulton. | Childhood caries is associated with poor health and a faster pace of ageing by midlife. | Dunedin Study |
| 28 | 2022 | Bourassa, K. J., Moffitt, T. E. Ambler, A., Hariri, A., Harrington, H. L., Houts, R. M., Ireland, D., Knodt, A., Poulton, R., Ramrakha, S., Caspi, A. | Accelerated aging in midlife is antedated by treatable adolescent health conditions. JAMA Pediatrics https://jamanetwork.com/journals/jamapediatrics/fullarticle/2789349 | Dunedin Study |
| 29 | 2022 | Meier, Madeline H., Avshalom Caspi, Antony Ambler, Ahmad R. Hariri, HonaLee Harrington, Sean Hogan, Renate Houts, Annchen R. Knodt, Sandhya Ramrakha, Leah Richmond-Rakerd, Richie Poulton Terrie E. Moffitt | Long-term Cannabis Users’ Preparedness for Healthy Aging: A Population-Representative Longitudinal Study. Lancet Healthy Longevity | Dunedin Study |
| 30 | 2022 | Langevin, S., Caspi, A., Barnes, J.C, Brennan, G., Poulton, R., Purdy, S., Tanksley, P.T., Thorne, P., Wilson, G., & Moffitt, T.E. | Life-course persistent antisocial behavior and accelerated biological aging in a longitudinal birth cohort. International Journal of Environmental Research and Public Health | Dunedin Study |
| 31 | 2022 | Bourassa, K. J., Caspi, A., Hall, K. S., Harrington, H. L., Houts, R. M., Kimbrel, N. A., Poulton, R., Ramrakha, S., Taylor, G. A., & Moffitt, T. E. | Which measures of stress best predict accelerated biological aging? Comparing perceived stress, stressful life events, and posttraumatic stress disorder. Under review. | Dunedin Study |
| 32 | 2021 | Simons, R. L., Lei, M.-K., Klopach, E., Berg, M., Zhang, Y., & Beach, S. S. R. | (Re)Setting Epigenetic Clocks: An Important Avenue Whereby Social Conditions Become Biologically Embedded across the Life Course. Journal of Health and Social Behavior, 62(3), 436–453. https://doi.org/10.1177/00221465211009309. | The FACHS Study (Family and Community Health Study of African American Families) |
| 33 | 2021 | Kuo, P.-L., Moore, A. Z., Lin, F. R., & Ferrucci, L. | Epigenetic age acceleration and hearing: Observations from the Baltimore Longitudinal Study of Aging. Frontiers in Aging Neuroscience, 13, Article 790926. https://doi.org/10.3389/fnagi.2021.790926. | BLSA (Baltimore Longitudinal Study of Aging) |
| 34 | 2021 | Wertz, Jasmin, Avshalom Caspi, Antony Ambler, Jonathan Broadbent, Robert J. Hancox, HonaLee Harrington, Renate M. Houts, Joan H. Leung, Richie Poulton, Suzanne C. Purdy, Sandhya Ramrakha, Line Jee Hartmann Rasmussen, Leah S. Richmond-Rakerd, Peter R. Thorne, Graham A. Wilson, Terrie E. Moffitt | History of psychiatric illness as a risk factor for accelerated aging: Evidence from a population-representative longitudinal cohort study. JAMA-Psychiatry | Dunedin Study |
| 35 | 2021 | Elliott, Max, Avshalom Caspi, RM Houts, A Ambler, JM Broadbent, RJ Hancox, HL Harrinton, S Hogan, R Keenan, A Knodt, JH Leung, TR Melzer, SC Purdy, S Ramrakha, LS Richmond-Rakerd, A Righarts, K Sugden, WM Thomson, PR thorne, BS Williams, G Wilson, AR Hariri, R Poulton, TE Moffitt . | Disparities in the pace of biological aging among midlife adults of the same chronological age: Implications for future frailty risk and policy. Nature Aging https://rdcu.be/cgNdq | Dunedin Study |
| 36 | 2021 | Richmond-Rakerd, Leah S., Avshalom Caspi, Antony Ambler Tracy d’Arbeloff, Marieke de Bruine, Maxwell Elliott, HonaLee Harrington, Sean Hogan, Renate M. Houts, David Ireland, Ross Keenan, Annchen R. Knodt, Tracy R. Melzer, Sena Park, Richie Poulton, Sandhya Ramrakha, Line Jee Hartmann Rasmussen, Elizabeth Sack, Adam T. Schmidt, Maria L. Sison, Jasmin Wertz, Ahmad R. Hariri, & Terrie E. Moffitt | Childhood self-control forecasts the pace of midlife aging and preparedness for old age. PNAS https://www.pnas.org/content/118/3/e2010211118 | Dunedin Study |
| 37 | 2020 | Belsky, DW, A Caspi, L Arseneault, Baccarelli, A., D Corcoran, Gao, X, Hannon, HL Harrington, L J Rassmussen, R Houts, K Huffman, WE Kraus, Kwon, D, J Mill, C Pieper, J Prinz, R Poulton, Schwartz, J, K Sugden, Vokonas, P, B Williams, TE Moffitt | Quantification of the pace of biological aging in humans through a blood test: The DunedinPoAm DNA methylation algorithm, eLife https://elifesciences.org/articles/54870 | Dunedin Study, Normative Aging Study, E-Risk Study, UK Understanding Society |
| 38 | 2020 | Bourassa, K. J., Caspi, A., Harrington, H. L., Houts, R. M., Poulton, R., Ramrakha, S., & Moffitt, T. E. . | Intimate partner violence and lower relationship quality are associated with faster biological aging. Psychology and Aging | Dunedin Study |
| 39 | 2020 | Rasmussen, LJ, A Caspi, A Ambler, A Danese, M Elliott, J Eugen-olsen, A Hariri, HL Harrington, R Houts, R Poulton, S Ramrakha, K Sugden, B Williams, TE Moffitt. | Association between elevated suPAR, a new biomarker of chronic inflammation, and accelerated aging. Journal of Gerontology, Medical Sciences | Dunedin Study |
| 40 | 2019 | Elliott, Maxwell L. Daniel W. Belsky, Annchen R. Knodt, David Ireland, Tracy R. Melzer, Richie Poulton, Sandhya Ramrakha, Avshalom Caspi, Terrie E. Moffitt, Ahmad R. Hariri | Brain-age in midlife is associated with accelerated biological aging and cognitive decline in a longitudinal birth-cohort. Molecular Psychiatry | Dunedin Study |
| 41 | 2017 | Belsky DW, Caspi A, Cohen HJ, Kraus WE, Ramrakha S, Poulton R, Moffitt TE. | Impact of Early Personal History Characteristics on the Pace of Aging: Implications for Clinical Trials of Therapies to Slow Aging and Extend Healthspan. Aging Cell. | Dunedin Study |
| 42 | 2017 | Belsky DW, Moffitt TE, Cohen AA, Corcoran DL, Levine ME, Prinz J, Schaefer J, Sugden K, Williams B, Poulton R, Caspi A. | Eleven telomere, epigenetic clock, and biomarker-composite quantifications of biological aging: Do they measure the same thing? American J of Epidemiology. doi: http://dx.doi.org/10.1101/071373 | Dunedin Study |
| 43 | 2015 | Belsky DW, Caspi A, Houts R, Cohen HJ, Corcoran D, Danese A, Harrington HL, Israel S, Levine ME, Schaefer J, Sugden K, Williams B, Yashin A, Poulton R, Moffitt TE. | Quantification of biological aging in young adults. Proceedings of the National Academy of Sciences of the United States of America. 77, 601-617. | Dunedin Study |
| 44 | Simons, R., Ong, M., Lei, M., Klopack, E., Berg, M., Zhang, Y., Philibert, R., & Beach, S. | Unstable Childhood, Adult Adversity, and Smoking Accelerate Biological Aging Among Middle-Age African Americans: Similar Findings for GrimAge and PoAm. J of Aging and Health 089826432110436. 10.1177/08982643211043668. | The FACHS Study |
