Cited by 80

The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.

Rejuvant®, a potential life-extending compound formulation with alpha-ketoglutarate and vitamins, conferred an average 8 year reduction in biological aging, after an average of 7 months of use, in the TruAge DNA methylation test

Demidenko, Oleksandr, Barardo, Diogo, Budovskii, Valery, Finnemore, Robb, Palmer, Francis R., Kennedy, Brian K., Budovskaya, Yelena V.·2021·Aging

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.18632/aging.203736

Cites

8 of 8 references verified0 citation errors · 0 not checkable

Statistics

Figures

Figure 1 TruAge age estimation and baseline chronological age are linearly correlated. The TruAge biological age is highly (adjusted R-squared = 0.77) statistically significantly (p-value = 2x10-9) correlated with chronological age of the 42 patients.
Figure 1 TruAge age estimation and baseline chronological age are linearly correlated. The TruAge biological age is highly (adjusted R-squared = 0.77) statistically significantly (p-value = 2x10-9) correlated with chronological age of the 42 patients.CC BY · PubMed Central
Figure 2 TruAge age estimation and baseline chronological age are linearly correlated, in both genders. The x-axis depicts DNA methylation age estimated by TruAge, and the y-axis the chronological age of the 42 patients at baseline. The pink and blue linear correlation plots depict the similarly high statistically significant linear correlation between both axes, for female and male patients, respectively.
Figure 2 TruAge age estimation and baseline chronological age are linearly correlated, in both genders. The x-axis depicts DNA methylation age estimated by TruAge, and the y-axis the chronological age of the 42 patients at baseline. The pink and blue linear correlation plots depict the similarly high statistically significant linear correlation between both axes, for female and male patients, respectively.CC BY · PubMed Central
Figure 3 CaAKG decreased methylation age among a homogeneous sub-population. The paired box plots represent the treatment effects at the patient and group level (n=13), between baseline and end of the trial (which on average had a duration of 7 months). The box plots depict the median and the 25th and 75th quartiles.
Figure 3 CaAKG decreased methylation age among a homogeneous sub-population. The paired box plots represent the treatment effects at the patient and group level (n=13), between baseline and end of the trial (which on average had a duration of 7 months). The box plots depict the median and the 25th and 75th quartiles.CC BY · PubMed Central
Figure 4 CaAKG decreased methylation age in the entire cohort. The paired box plots represent the treatment effects at the patient and group level (n=42), between baseline and end of the trial (which on average had a duration of 7 months). The box plots depict the median and the 25th and 75th quartiles.
Figure 4 CaAKG decreased methylation age in the entire cohort. The paired box plots represent the treatment effects at the patient and group level (n=42), between baseline and end of the trial (which on average had a duration of 7 months). The box plots depict the median and the 25th and 75th quartiles.CC BY · PubMed Central
Figure 5 CaAKG decreases methylation age regardless of gender. The image displays the effects of CaAKG on methylation age between the start and end of treatment, broken down by gender. For each gender, the red and blue paired box plots, depict the data at baseline and endpoint, respectively. The boxplots are based on the median and the 25th and 75th quartiles.
Figure 5 CaAKG decreases methylation age regardless of gender. The image displays the effects of CaAKG on methylation age between the start and end of treatment, broken down by gender. For each gender, the red and blue paired box plots, depict the data at baseline and endpoint, respectively. The boxplots are based on the median and the 25th and 75th quartiles.CC BY · PubMed Central
Figure 6 3D scatter plot of the covariates influencing CaAKG treatment efficacy. The x-axis refers to the difference, in years, between the chronological and biological ages at baseline (a patient in an older state will have larger positive value). The y-axis depicts the effect of CaAKG treatment in terms of years that the methylation age decreased (higher values indicate larger benefits). The z-axis is the chronological age at baseline (older patients will be “deeper”). The 3D plane was traced by using the linear regression of both covariates to treatment effect.
Figure 6 3D scatter plot of the covariates influencing CaAKG treatment efficacy. The x-axis refers to the difference, in years, between the chronological and biological ages at baseline (a patient in an older state will have larger positive value). The y-axis depicts the effect of CaAKG treatment in terms of years that the methylation age decreased (higher values indicate larger benefits). The z-axis is the chronological age at baseline (older patients will be “deeper”). The 3D plane was traced by using the linear regression of both covariates to treatment effect.CC BY · PubMed Central

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Research works printed but not linked: 25

  1. Franzke, 2016, Biomarkers of Aging: From Function to Molecular Biology., Nutrients, 8, 338, 10.3390/nu8060338 · DOI
  2. Kuchel, 2018, A framework for selection of blood-based biomarkers for geroscience-guided clinical trials: report from the TAME Biomarkers Workgroup., Geroscience, 4, 419, 10.1007/s11357-018-0042-y · DOI · Paper page
  3. Kennedy, 2020, Aging Biomarkers: From Functional Tests to Multi-Omics Approaches., Proteomics, 2, e1900408, 10.1002/pmic.201900408 · DOI
  4. Heard, 2019, Advances in epigenetics link genetics to the environment and disease., Nature, 57, 489, 10.1038/s41586-019-1411-0 · DOI · Paper page
  5. Richardson, 2019, The role of DNA methylation in epigenetics of aging., Pharmacol Ther, 19, 172, 10.1016/j.pharmthera.2018.11.001 · DOI
  6. Rajapakse, 2013, Genome-wide methylation profiles reveal quantitative views of human aging rates., Mol Cell, 4, 359, 10.1016/j.molcel.2012.10.016 · DOI · Paper page
  7. Robertson, 2011, DNA methylation: superior or subordinate in the epigenetic hierarchy?, Genes Cancer, 2, 607, 10.1177/1947601910393957 · DOI
  8. Beck, 2013, Age-associated epigenetic drift: implications, and a case of epigenetic thrift?, Hum Mol Genet, 2, R7, 10.1093/hmg/ddt375 · DOI
  9. Brenner, 2016, Frailty is associated with the epigenetic clock but not with telomere length in a German cohort., Clin Epigenetics, 8, 21, 10.1186/s13148-016-0186-5 · DOI
  10. Murphy, 2015, The epigenetic clock is correlated with physical and cognitive fitness in the Lothian Birth Cohort 1936., Int J Epidemiol, 4, 1388, 10.1093/ije/dyu277 · DOI
  11. Sipos, 2014, Obesity accelerates epigenetic aging of human liver., Proc Natl Acad Sci USA, 11, 15538, 10.1073/pnas.1412759111 · DOI
  12. Franceschi, 2015, Accelerated epigenetic aging in Down syndrome., Aging Cell, 1, 491, 10.1111/acel.12325 · DOI
  13. Pinti, 2017, Basic science and pathogenesis of ageing with HIV: potential mechanisms and biomarkers., AIDS, 3, S105, 10.1097/QAD.0000000000001441 · DOI
  14. Ritz, 2015, Increased epigenetic age and granulocyte counts in the blood of Parkinson’s disease patients., Aging (Albany NY), 7, 1130, 10.18632/aging.100859 · DOI
  15. Overvad, 2017, DNA methylome analysis identifies accelerated epigenetic ageing associated with postmenopausal breast cancer susceptibility., Eur J Cancer, 7, 299, 10.1016/j.ejca.2017.01.014 · DOI
  16. Bhaumik, 2020, Alpha-Ketoglutarate, an Endogenous Metabolite, Extends Lifespan and Compresses Morbidity in Aging Mice., Cell Metab, 3, 447, 10.1016/j.cmet.2020.08.004 · DOI
  17. Yin, 2016, The glutamine-alpha-ketoglutarate (AKG) metabolism and its nutritional implications., Amino Acids, 4, 2067, 10.1007/s00726-016-2254-8 · DOI
  18. Li, 2016, Alpha-Ketoglutarate: Physiological Functions and Applications., Biomol Ther (Seoul), 2, 1, 10.4062/biomolther.2015.078 · DOI · Paper page
  19. Kandefer-Szerszeń, 2017, Alpha-Ketoglutarate as a Molecule with Pleiotropic Activity: Well-Known and Novel Possibilities of Therapeutic Use., Arch Immunol Ther Exp (Warsz), 6, 21, 10.1007/s00005-016-0406-x · DOI · Paper page
  20. Pierzynowski, 2008, Biological effects of 2-oxoglutarate with particular emphasis on the regulation of protein, mineral and lipid absorption/metabolism, muscle performance, kidney function, bone formation and cancerogenesis, all viewed from a healthy ageing perspective state of the art--review article., J Physiol Pharmacol, 5, 91
  21. Wang, 2014, The metabolite α-ketoglutarate extends lifespan by inhibiting ATP synthase and TOR., Nature, 51, 397, 10.1038/nature13264 · DOI
  22. Kennedy, 2014, Mouse models and aging: longevity and progeria., Curr Top Dev Biol, 10, 249, 10.1016/B978-0-12-397920-9.00003-2 · DOI
  23. Kaarniranta, 2014, Krebs cycle intermediates regulate DNA and histone methylation: epigenetic impact on the aging process., Ageing Res Rev, 1, 45, 10.1016/j.arr.2014.05.004 · DOI
  24. Runyon, 1977, Nonparametric statistics: a contemporary approach.
  25. Maechler, 2003, scatterplot3d - An R Package for Visualizing Multivariate Data., J Stat Softw, 8, 1, 10.18637/jss.v008.i11 · DOI

Cited by

Sorted by citations

See all 80

Metadata

Authors
Demidenko, O., Barardo, D., Budovskii, V., Finnemore, R., Palmer, F. R., Kennedy, B. K., & Budovskaya, Y. V.
Affiliations
  • TruMe Inc., Alameda, CA 94502, USA
  • Healthy Longevity Translational Research Programme, Yong Loo Lin School of Medicine, National University Singapore, Singapore 117456, Singapore
  • Ponce de Leon Health, Fernandina, FL 32034, USA
Venue
Aging
Published
2021-11-30
Volume
13
Issue
22
Pages
24485-24499
ISSN
1945-4589
Cited by
80
References
8
Language
en

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review