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Chronological Clocks


1st Generation Biological Clocks, research validated


2nd Generation Biological Clocks, research validated


3rd Generation Biological Clocks, research validated


Next Generation Biological Clocks, in development


Future Generation Biological Clocks, planned


Vishal Rao, Founder, Chairman and Chief Rejuvenation Officer, OptiHuman, Inc. at age 42
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GregorianAge - based on number calendar years since birth

Description

The GregorianAge is simply the number of Gregorian calendar years that have completed since an individual's birth. It is the calendar that is used in most of the world and calibrated such that 1 year is exactly one complete revolution of the Earth around the Sun.



TelomereAge - based on telomere length

Most Recent Result

Longitudinal Results

Only one measurement tracked so far; therefore longitudinal results are not available.

Description

Telomere length is affected by both genetic and epigenetic contributions. A new study found that DNA methylation is closely linked to TL. The study by researchers at the University of California Los Angeles shows a very significant linkage between two different markers that indicate aging [2].

Telomeres are an essential part of human cells that affect how our cells age [1]. Telomere length has emerged as an important determinant of replicative senescence and cell fate - an important indicator of the aging process and a wide range of disease states, including cancers, cardiovascular disease, and age-related disorders.

Shorter telomeres are not only associated with age but with disease too. In fact, shorter telomere length and low telomerase activity are associated with several chronic preventable diseases. These include hypertension, cardiovascular disease,insulin resistance, type 2 diabetes, depression, osteoporosis, and obesity.

Shorter telomeres have also been implicated in genomic instability and oncogenesis. Older people with shorter telomeres have three and eight times increased risk to die from heart and infectious diseases, respectively [4].

The rate of telomere shortening and telomere length is therefore critical to an individual's health and pace of aging.

Source: TruDiagnostic

References

  1. Jaskelioff, M., Muller, F. L., Paik, J.-H., Thomas, E., Jiang, S., Adams, A. C., Sahin, E., Kost-Alimova, M., Protopopov, A., Cadiñanos, J., Horner, J. W., Maratos-Flier, E., & DePinho, R. A. (2011). Telomerase reactivation reverses tissue degeneration in aged telomerase-deficient mice. Nature, 469(7328), 102-106.
  2. Lee, Y., Sun, D., Ori, A. P. S., Lu, A. T., Seeboth, A., Harris, S. E., Deary, I. J., Marioni, R. E., Soerensen, M., Mengel-From, J., Hjelmborg, J., Christensen, K., Wilson, J. G., Levy, D., Reiner, A. P., Chen, W., Li, S., Harris, J. R., Magnus, P., ... Horvath, S. (2019). Epigenome-wide association study of leukocyte telomere length. Aging, 11(16), 5876-5894.
  3. Lu, A. T., Seeboth, A., Tsai, P.-C., Sun, D., Quach, A., Reiner, A. P., Kooperberg, C., Ferrucci, L., Hou, L., Baccarelli, A. A., Li, Y., Harris, S. E., Corley, J., Taylor, A., Deary, I. J., Stewart, J. D., Whitsel, E. A., Assimes, T. L., Chen, W., ... Horvath, S. (2019). DNA methylation-based estimator of telomere length. Aging, 11(16), 5895-5923.
  4. Shammas M. A. (2011). Telomeres, lifestyle, cancer, and aging. Current opinion in clinical nutrition and metabolic care, 14(1), 28-34.
  5. Songyang, Z. (2017). Introduction to Telomeres and Telomerase. Methods in Molecular Biology (Clifton, N.J.), 1587, 1-13.
  6. Zvereva, M., Shcherbakova, D., & Dontsova, O. (2010). Telomerase: Structure, functions, and activity regulation. Biochemistry (00062979), 75(13), 1563-1583.


HorvathAge - based on DNA methylation, without considering immune system

Most Recent Result

Longitudinal Results

Only one measurement tracked so far; therefore longitudinal results are not available.

Description

Intrinsic epigenetic measures "pure" epigenetic aging effects that are not confounded by differences in immune cell counts. Intrinsic epigenetic age (IEA) is determined by controlling for chronological age and various blood immune cell counts (naïve CD8+ T cells, exhausted CD8+ T cells, plasma B cells, CD4+ T cells, natural killer cells, monocytes, and granulocytes). The measure of IEA is an incomplete measure of the age-related functional decline of the immune system because it does not track age-related changes in blood cell composition, such as the decrease of naïve CD8+ T cells and the increase in memory or exhausted CD8+ T cells.

Source: TruDiagnostic

References

  1. Chen, B. H., Marioni, R. E., Colicino, E., Peters, M. J., Ward-Caviness, C. K., Tsai, P. C., Roetker, N. S., Just, A. C., Demerath, E. W., Guan, W., Bressler, J., Fornage, M., Studenski, S., Vandiver, A. R., Moore, A. Z., Tanaka, T., Kiel, D. P., Liang, L., Vokonas, P., Schwartz, J., ... Horvath, S. (2016). DNA methylation-based measures of biological age: meta-analysis predicting time to death. Aging, 8(9), 1844-1865. https://doi.org/10.18632/aging.101020
  2. Gibson, J., Russ, T. C., Clarke, T.-K., Howard, D. M., Hillary, R. F., Evans, K. L., Walker, R. M., Bermingham, M. L., Morris, S. W., Campbell, A., Hayward, C., Murray, A. D., Porteous, D. J., Horvath, S., Lu, A. T., McIntosh, A. M., Whalley, H. C., & Marioni, R. E. (2019). A meta-analysis of genome-wide association studies of epigenetic age acceleration. PLOS Genetics, 15(11), e1008104. https://doi.org/10.1371/journal.pgen.1008104
  3. Horvath, S., Gurven, M., Levine, M. E., Trumble, B. C., Kaplan, H., Allayee, H., Ritz, B. R., Chen, B., Lu, A. T., Rickabaugh, T. M., Jamie- son, B. D., Sun, D., Li, S., Chen, W., Quintana-Murci, L., Fagny, M., Kobor, M. S., Tsao, P. S., Reiner, A. P., Edlefsen, K. L., ... Assimes, T. L. (2016). An epigenetic clock analysis of race/ethnicity, sex, and coronary heart disease. Genome biology, 17(1), 171. https://doi.org/10.1186/s13059-016-1030-0
  4. Neeland, I. J., Poirier, P., & Després, J. P. (2018). Cardiovascular and Metabolic Heterogeneity of Obesity: Clinical Challenges and Implications for Management. Circulation, 137(13), 1391-1406. https://doi.org/10.1161/CIRCULATIONAHA.117.029617
  5. Okazaki, S., Numata, S., Otsuka, I., Horai, T., Kinoshita, M., Sora, I., Ohmori, T., & Hishimoto, A. (2020). Decelerated epigenetic aging associated with mood stabilizers in the blood of patients with bipolar disorder. Translational Psychiatry, 10(1), 129. https://doi.org/10.1038/s41398-020-0813-y
  6. Pawelec G. (1999). Immunosenescence: impact in the young as well as the old?. Mechanisms of ageing and development, 108(1), 1-7. https://doi.org/10.1016/s0047-6374(99)00010-x
  7. Quach, A., Levine, M. E., Tanaka, T., Lu, A. T., Chen, B. H., Ferrucci, L., Ritz, B., Bandinelli, S., Neuhouser, M. L., Beasley, J. M., Snetse- laar, L., Wallace, R. B., Tsao, P. S., Absher, D., Assimes, T. L., Stewart, J. D., Li, Y., Hou, L., Baccarelli, A. A., Whitsel, E. A., ... Horvath, S. (2017). Epigenetic clock analysis of diet, exercise, education, and lifestyle factors. Aging, 9(2), 419-446. https://doi.org/10.18632/aging.101168
  8. Roberts-Thomson, I. C., Whittingham, S., Youngchaiyud, U., & Mackay, I. R. (1974). Ageing, immune response, and mortality. Lancet (London, England), 2(7877), 368-370. https://doi.org/10.1016/s0140-6736(74)91755-3
  9. Smith, J. A., Raisky, J., Ratliff, S. M., Liu, J., Kardia, S. L. R., Turner, S. T., Mosley, T. H., & Zhao, W. (2019). Intrinsic and extrinsic epigenetic age acceleration are associated with hypertensive target organ damage in older African Americans. BMC Medical Genomics, 12(1), 141. https://doi.org/10.1186/s12920-019-0585-5

Supplemental Information Selected by Vishal



HannumAge - based on DNA methylation, including immune system

Most Recent Result

Longitudinal Results

Only one measurement tracked so far; therefore longitudinal results are not available.

Description

Extrinsic epigenetic age (EEA) applies to whole blood and aims to measure epigenetic aging in immune-related components. EEA has a positive correlation with the amount of exhausted CD8+ T cells and plasma B cells and a negative correlation with the amount of naïve CD8+ T cells. Blood cell counts were estimated based on DNA methylation data. EEA tracks both age-related changes in blood cell composition and intrinsic epigenetic changes. It can often be a better predictor of outcomes like death and is an overall reading of the strength of your immune system.

Source: TruDiagnostic

References

  1. Chen, B. H., Marioni, R. E., Colicino, E., Peters, M. J., Ward-Caviness, C. K., Tsai, P. C., Roetker, N. S., Just, A. C., Demerath, E. W., Guan, W., Bressler, J., Fornage, M., Studenski, S., Vandiver, A. R., Moore, A. Z., Tanaka, T., Kiel, D. P., Liang, L., Vokonas, P., Schwartz, J., ... Horvath, S. (2016). DNA methylation-based measures of biological age: meta-analysis predicting time to death. Aging, 8(9), 1844-1865. https://doi.org/10.18632/aging.101020
  2. Gibson, J., Russ, T. C., Clarke, T.-K., Howard, D. M., Hillary, R. F., Evans, K. L., Walker, R. M., Bermingham, M. L., Morris, S. W., Campbell, A., Hayward, C., Murray, A. D., Porteous, D. J., Horvath, S., Lu, A. T., McIntosh, A. M., Whalley, H. C., & Marioni, R. E. (2019). A meta-analysis of genome-wide association studies of epigenetic age acceleration. PLOS Genetics, 15(11), e1008104. https://doi.org/10.1371/journal.pgen.1008104
  3. Horvath, S., Gurven, M., Levine, M. E., Trumble, B. C., Kaplan, H., Allayee, H., Ritz, B. R., Chen, B., Lu, A. T., Rickabaugh, T. M., Jamie- son, B. D., Sun, D., Li, S., Chen, W., Quintana-Murci, L., Fagny, M., Kobor, M. S., Tsao, P. S., Reiner, A. P., Edlefsen, K. L., ... Assimes, T. L. (2016). An epigenetic clock analysis of race/ethnicity, sex, and coronary heart disease. Genome biology, 17(1), 171. https://doi.org/10.1186/s13059-016-1030-0
  4. Neeland, I. J., Poirier, P., & Després, J. P. (2018). Cardiovascular and Metabolic Heterogeneity of Obesity: Clinical Challenges and Implications for Management. Circulation, 137(13), 1391-1406. https://doi.org/10.1161/CIRCULATIONAHA.117.029617
  5. Okazaki, S., Numata, S., Otsuka, I., Horai, T., Kinoshita, M., Sora, I., Ohmori, T., & Hishimoto, A. (2020). Decelerated epigenetic aging associated with mood stabilizers in the blood of patients with bipolar disorder. Translational Psychiatry, 10(1), 129. https://doi.org/10.1038/s41398-020-0813-y
  6. Pawelec G. (1999). Immunosenescence: impact in the young as well as the old?. Mechanisms of ageing and development, 108(1), 1-7. https://doi.org/10.1016/s0047-6374(99)00010-x
  7. Quach, A., Levine, M. E., Tanaka, T., Lu, A. T., Chen, B. H., Ferrucci, L., Ritz, B., Bandinelli, S., Neuhouser, M. L., Beasley, J. M., Snetse- laar, L., Wallace, R. B., Tsao, P. S., Absher, D., Assimes, T. L., Stewart, J. D., Li, Y., Hou, L., Baccarelli, A. A., Whitsel, E. A., ... Horvath, S. (2017). Epigenetic clock analysis of diet, exercise, education, and lifestyle factors. Aging, 9(2), 419-446. https://doi.org/10.18632/aging.101168
  8. Roberts-Thomson, I. C., Whittingham, S., Youngchaiyud, U., & Mackay, I. R. (1974). Ageing, immune response, and mortality. Lancet (London, England), 2(7877), 368-370. https://doi.org/10.1016/s0140-6736(74)91755-3
  9. Smith, J. A., Raisky, J., Ratliff, S. M., Liu, J., Kardia, S. L. R., Turner, S. T., Mosley, T. H., & Zhao, W. (2019). Intrinsic and extrinsic epigenetic age acceleration are associated with hypertensive target organ damage in older African Americans. BMC Medical Genomics, 12(1), 141. https://doi.org/10.1186/s12920-019-0585-5

Supplemental Information Selected by Vishal



PhenoAge - based on a few common clinical biomarkers

Most Recent Result

Longitudinal Results

Only one measurement tracked so far; therefore longitudinal results are not available.

Description

The PhenoAge Test gives you a biological age score. It is based on levels of 9 clinical biomarkers circulating in blood, which were found to strongly correlate with onset of age-related diseases and mortality. It is a biomarker of aging developed in 2018 by a team including Dr. Morgan Levine, Dr. Luigi Ferrucci, and Dr. Steve Horvath. Along with your biological age score, this test includes many well-established clinical biomarkers used by physicians and in clinical trials and can be useful for understanding your health status.

Source: Clock Foundation

References

  1. Levine, M. E., Lu, A. T., Quach, A., Chen, B. H., Assimes, T. L., Bandinelli, S., Hou, L., Baccarelli, A. A., Stewart, J. D., Li, Y., Whitsel, E. A., Wilson, J. G., Reiner, A. P., Aviv, A., Lohman, K., Liu, Y., Ferrucci, L., & Horvath, S. (2018). An epigenetic biomarker of aging for lifespan and healthspan. Aging (Albany NY), 10(4), 573-591.
  2. Ferrucci, L., Gonzalez-Freire, M., Fabbri, E., Simonsick, E., Tanaka, T., Moore, Z. et al. (2020). Measuring biological aging in humans: A quest. Aging Cell, 19(2), e13080.


AnthropoAge - based on anthropometric measures to predict all cause mortality

Most Recent Result

Longitudinal Results

Only one measurement tracked so far; therefore longitudinal results are not available.

Description

AnthropoAge considers the sex-dependent role of anthropometry for prediction of 10-year all-cause mortality using data from 18,794 NHANES participants to generate and validate a new biological age metric.

Source: Abstract from original AnthropoAge paper.

References

  1. Fermin-Martinez, C. A., Marquez-Salinas, A., Guerra, E. C., Zavala-Romero, L., Antonio-Villa, N.E., Fernandez-Chirino, L., Sandoval-Colin, E., Barquera-Guevara, D.A., Munoz, A.C., Vargas-Vazuez, A., Paz-Cabrera, C.D., Ramirez-Garcia, D., Gutierrez-Robledo, L.M., Bello-Chavolla, O.Y. AnthropoAge, a novel approach to integrate body composition into the estimation of biological age.Aging Cell doi: 10.1111/acel.13756.


GrimAge - based on multiple epigenetic factors predictive of end of life

Most Recent Result

Longitudinal Results

Only one measurement tracked so far; therefore longitudinal results are not available.

Description

Your GrimAge refers to your calculated epigenetic biological age. Our test measures specific sites where your DNA has been modified by methyl groups and then looks at correlation patterns in epigenetic markers to estimate your actual biological age. The GrimAge measure of biological age has now been shown to strongly predict future healthspan and lifespan.

Your percentile rank refers to how you compare with others in a frequency distribution. So if you scored a percentile rank of 87 on your biological age test, it would mean that your biological age is younger than 87% of others of the same chronological age. By contrast, if you scored 50% or less, that means your biological age is higher than average compared to others of your same chronological age.

Source: Clock Foundation

References

  1. Lu AT, Quach A, Wilson JG,..., Horvath S, DNA methylation GrimAge strongly predicts lifespan and healthspan. Aging (Albany NY).2019;11(2):303-327. doi:10.18632/aging.10168


GlycanAge - based on glycation of immune cells

Most Recent Result

Longitudinal Results

Only one measurement tracked so far; therefore longitudinal results are not available.

Description

GlycanAge is a scientifically proven measurement tool. It responds quickly to lifestyle changes, allowing you to measure their impact. It works by measuring chronic inflammation in your immune system at the molecular level - also known as inflammaging. Your biological aging is influenced by your genes, age, and lifestyle. GlycanAge measures how your lifestyle choices affect the activity of your immune system.

Source: GlycanAge Results Report

References

  1. Menni C, Gudelj I, Macdonald-Dunlop E, Mangino M, Zierer J, Bešić E, Joshi PK, Trbojević-Akmačić I, Chowienczyk PJ, Spector TD, Wilson JF, Lauc G, Valdes AM.Glycosylation Profile of Immunoglobulin G Is Cross-Sectionally Associated With Cardiovascular Disease Risk Score and Subclinical Atherosclerosis in Two Independent Cohorts. Circ Res. 2018 May 25;122(11):1555-1564. doi: 10.1161/CIRCRESAHA.117.312174. Epub 2018 Mar 13. PMID: 29535164; PMCID: PMC5970566.
  2. Martin TC, Šimurina M, Ząbczyńska M, Martinic Kavur M, Rydlewska M, Pezer M, Kozłowska K, Burri A, Vilaj M, Turek-Jabrocka R, Krnjajić-Tadijanović M, Trofimiuk-Müldner M, Ugrina I, Lityńska A, Hubalewska-Dydejczyk A, Trbojevic-Akmacic I, Lim EM, Walsh JP, Pocheć E, Spector TD, Wilson SG, Lauc G. Decreased Immunoglobulin G Core Fucosylation, A Player in Antibody-dependent Cell-mediated Cytotoxicity, is Associated with Autoimmune Thyroid Diseases. Mol Cell Proteomics. 2020 May;19(5):774-792. doi: 10.1074/mcp.RA119.001860. Epub 2020 Feb 5. PMID: 32024769; PMCID: PMC7196582.
  3. Sarin Heikki V., Gudelj Ivan, Honkanen Jarno, Ihalainen Johanna K., Vuorela Arja, Lee Joseph H., Jin Zhenzhen, Terwilliger Joseph D., Isola Ville, Ahtiainen Juha P., Häkkinen Keijo, Jurić Julija, Lauc Gordan, Kristiansson Kati, Hulmi Juha J., Perola Markus Molecular Pathways Mediating Immunosuppression in Response to Prolonged Intensive Physical Training, Low-Energy Availability, and Intensive Weight Loss. Frontiers in Immunology. 2019; 10. doi: 10.3389/fimmu.2019.00907 ISSN:1664-3224
  4. Bondt, A.; Selman, M. H.; Deelder, A. M.; Hazes, J. M.; Willemsen, S. P.; Wuhrer, M.; Dolhain, R. J. Association between galactosylation of immunoglobulin G and improvement of rheumatoid arthritis during pregnancy is independent of sialylation. J. Proteome Res. 2013, 12 (10), 4522– 4531, DOI: 10.1021/pr400589m
  5. Qin, R., Yang, Y., Chen, H. et al. Prediction of neoadjuvant chemotherapeutic efficacy in patients with locally advanced gastric cancer by serum IgG glycomics profiling. Clin Proteom 17, 4 (2020). https://doi.org/10.1186/s12014-020-9267-8
  6. C. Pilkington, E. Yeung, D. Isenberg, A. K. Lefvert & G. A. W. Rook (1995) Agalactosyl IgG and Antibody Specificity in Rheumatoid Arthritis, Tuberculosis, Systemic Lupus Erythematosus and Myasthenia Gravis, Autoimmunity, 22:2, 107-111, DOI: 10.3109/08916939508995306
  7. Clemens Wittenbecher, Tamara Štambuk, Olga Kuxhaus, Najda Rudman, Frano Vučković, Jerko Štambuk, Catarina Schiborn, Dario Rahelić, Stefan Dietrich, Olga Gornik, Markus Perola, Heiner Boeing, Matthias B. Schulze, Gordan Lauc; Plasma N-Glycans as Emerging Biomarkers of Cardiometabolic Risk: A Prospective Investigation in the EPIC-Potsdam Cohort Study. Diabetes Care 1 March 2020; 43 (3): 661–668. https://doi.org/10.2337/dc19-1507
  8. Peng J, Vongpatanasin W, Sacharidou A, Kifer D, Yuhanna IS, Banerjee S, Tanigaki K, Polasek O, Chu H, Sundgren NC, Rohatgi A, Chambliss KL, Lauc G, Mineo C, Shaul PW. Supplementation With the Sialic Acid Precursor N-Acetyl-D-Mannosamine Breaks the Link Between Obesity and Hypertension. Circulation. 2019 Dec 10;140(24):2005-2018. doi: 10.1161/CIRCULATIONAHA.119.043490. Epub 2019 Oct 10. PMID: 31597453; PMCID: PMC7027951.
  9. Vučković, F., Krištić, J., Gudelj, I., Teruel, M., Keser, T., Pezer, M., Pučić-Baković, M., Štambuk, J., Trbojević-Akmačić, I., Barrios, C., Pavić, T., Menni, C., Wang, Y., Zhou, Y., Cui, L., Song, H., Zeng, Q., Guo, X., Pons-Estel, B.A., McKeigue, P., Leslie Patrick, A., Gornik, O., Spector, T.D., Harjaček, M., Alarcon-Riquelme, M., Molokhia, M., Wang, W. and Lauc, G. (2015), Association of Systemic Lupus Erythematosus With Decreased Immunosuppressive Potential of the IgG Glycome. Arthritis & Rheumatology, 67: 2978-2989. https://doi.org/10.1002/art.39273
  10. Zou, X.; Yao, F.; Yang, F.; Zhang, F.; Xu, Z.; Shi, J.; Kuno, A.; Zhao, H.; Zhang, Y. Glycomic Signatures of Plasma IgG Improve Preoperative Prediction of the Invasiveness of Small Lung Nodules. Molecules 2020, 25, 28. https://doi.org/10.3390/molecules25010028
  11. Irena Trbojević Akmačić, Nicholas T. Ventham, Evropi Theodoratou, Frano Vučković, Nicholas A. Kennedy, Jasminka Krištić, Elaine R. Nimmo, Rahul Kalla, Hazel Drummond, Jerko Štambuk, Malcolm G. Dunlop, Mislav Novokmet, Yurii Aulchenko, Olga Gornik, IBD-BIOM Consortium, Harry Campbell, Maja Pučić Baković, Jack Satsangi, Gordan Lauc, Inflammatory Bowel Disease Associates with Proinflammatory Potential of the Immunoglobulin G Glycome, Inflammatory Bowel Diseases, Volume 21, Issue 6, 1 June 2015, Pages 1237–1247, https://doi.org/10.1097/MIB.0000000000000372
  12. Mads Delbo Larsen et al., Afucosylated IgG characterizes enveloped viral responses and correlates with COVID-19 severity. Science371,eabc8378(2021).DOI:10.1126/science.abc8378
  13. Lauc G. Precision medicine that transcends genomics: Glycans as integrators of genes and environment. Biochim Biophys Acta. 2016 Aug;1860(8):1571-3. doi: 10.1016/j.bbagen.2016.05.001. Epub 2016 May 4. PMID: 27155579.
  14. Tijardović Marko, Marijančević Domagoj, Bok Daniel, Kifer Domagoj, Lauc Gordan, Gornik Olga, Keser Toma. Intense Physical Exercise Induces an Anti-inflammatory Change in IgG N-Glycosylation Profile. Frontiers in Physiology.10: 2019. 10.3389/fphys.2019.01522 ISSN:1664-042X
  15. Selman MH, de Jong SE, Soonawala D, Kroon FP, Adegnika AA, Deelder AM, Hokke CH, Yazdanbakhsh M, Wuhrer M. Changes in antigen-specific IgG1 Fc N-glycosylation upon influenza and tetanus vaccination. Mol Cell Proteomics. 2012 Apr;11(4):M111.014563. doi: 10.1074/mcp.M111.014563. Epub 2011 Dec 19. PMID: 22184099; PMCID: PMC3322571. Download .nbib Format:
  16. Roosmarijn F.H. Lemmers, Marija Vilaj, Daniel Urda, Felix Agakov, Mirna Šimurina, Lucija Klaric, Igor Rudan, Harry Campbell, Caroline Hayward, Jim F. Wilson, Aloysius G. Lieverse, Olga Gornik, Eric J.G. Sijbrands, Gordan Lauc, Mandy van Hoek, IgG glycan patterns are associated with type 2 diabetes in independent European populations, Biochimica et Biophysica Acta (BBA) - General Subjects, Volume 1861, Issue 9, 2017, Pages 2240-2249, ISSN 0304-4165, https://doi.org/10.1016/j.bbagen.2017.06.020.
  17. Wong AH, Fukami Y, Sudo M, Kokubun N, Hamada S, Yuki N. Sialylated IgG-Fc: a novel biomarker of chronic inflammatory demyelinating polyneuropathy. J Neurol Neurosurg Psychiatry. 2016 Mar;87(3):275-9. doi: 10.1136/jnnp-2014-309964. Epub 2015 Mar 26. PMID: 25814494.
  18. Cvetko A, Kifer D, Gornik O, Klarić L, Visser E, Lauc G, Wilson JF, Štambuk T. Glycosylation Alterations in Multiple Sclerosis Show Increased Proinflammatory Potential. Biomedicines. 2020 Oct 13;8(10):410. doi: 10.3390/biomedicines8100410. PMID: 33065977; PMCID: PMC7599553.
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  20. Alyce C Russell, Mirna Šimurina, Monique T Garcia, Mislav Novokmet, Youxin Wang, Igor Rudan, Harry Campbell, Gordan Lauc, Meghan G Thomas, Wei Wang, The N-glycosylation of immunoglobulin G as a novel biomarker of Parkinson's disease, Glycobiology, Volume 27, Issue 5, May 2017, Pages 501–510, https://doi.org/10.1093/glycob/cwx022
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  22. Greto, V.L., Cvetko, A., Štambuk, T. et al. Extensive weight loss reduces glycan age by altering IgG N-glycosylation. Int J Obes 45, 1521–1531 (2021). https://doi.org/10.1038/s41366-021-00816-3
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  24. Cremata JA, Sorell L, Montesino R, Garcia R, Mata M, Cabrera G, Galvan JA, Garcia G, Valdes R, Garrote JA. Hypogalactosylation of serum IgG in patients with coeliac disease. Clin Exp Immunol. 2003 Sep;133(3):422-9. doi: 10.1046/j.1365-2249.2003.02220.x. PMID: 12930370; PMCID: PMC1808795.
  25. Novak J, Tomana M, Shah GR, Brown R, Mestecky J. Heterogeneity of IgG Glycosylation in Adult Periodontal Disease. Journal of Dental Research. 2005;84(10):897-901. doi:10.1177/154405910508401005
  26. Ercan A, Kohrt WM, Cui J, Deane KD, Pezer M, Yu EW, Hausmann JS, Campbell H, Kaiser UB, Rudd PM, Lauc G, Wilson JF, Finkelstein JS, Nigrovic PA. Estrogens regulate glycosylation of IgG in women and men. JCI Insight. 2017 Feb 23;2(4):e89703. doi: 10.1172/jci.insight.89703. PMID: 28239652; PMCID: PMC5313059.
  27. Theodoratou, E., Thaçi, K., Agakov, F. et al. Glycosylation of plasma IgG in colorectal cancer prognosis. Sci Rep 6, 28098 (2016). https://doi.org/10.1038/srep28098
  28. Gudelj,I., Lauc, G., Pezer, M., Immunoglobulin G glycosylation in aging and diseases, Cellular Immunology, Volume 333, 2018, Pages 65-79, ISSN 0008-8749, https://doi.org/10.1016/j.cellimm.2018.07.009.
  29. Vučković F, Theodoratou E, Thaçi K, Timofeeva M, Vojta A, Štambuk J, Pučić-Baković M, Rudd PM, Đerek L, Servis D, Wennerström A, Farrington SM, Perola M, Aulchenko Y, Dunlop MG, Campbell H, Lauc G. IgG Glycome in Colorectal Cancer. Clin Cancer Res. 2016 Jun 15;22(12):3078-86. doi: 10.1158/1078-0432.CCR-15-1867. Epub 2016 Feb 1. PMID: 26831718; PMCID: PMC5860729.


DunedinPace - pace of aging based on DNA methylation

Most Recent Result

Longitudinal Results

Only one measurement tracked so far; therefore longitudinal results are not available.

Description

The pace of aging in methylation tells how many years you are aging per year at a precise moment. With a single blood test, we are able to identify this. Acquiring your pace of aging gives insight into your current health and disease state. The goal is to have your rate of aging below 1.

The pace of aging algorithm not only provides benefits to the individual tested but it also has application for clinical studies. The DunedinPACE test provides an alternative way of measuring whether age-slowing treatments may work. It is sensitive to health interventions and will allow faster testing of treatment intended to extend healthspan in humans. The more data collected on individuals with accelerated or slowed aging can potentially help reveal the mechanisms of aging and how some individuals are more adversely affected by their lifestyle and environment than others. DunedinPACE will help public health officials test whether policies of programs have the power to help people lead a longer, healthier life.

Source: TruDiagnostic

References

  1. About Us: The Dunedin Study - Dunedin Multidisciplinary Health & Development Research Unit. The Dunedin Study - DMHDRU.
  2. Bell, C. G., Lowe, R., Adams, P. D., Baccarelli, A. A., Beck, S., Bell, J. T., Christensen, B. C., Gladyshev, V. N., Heijmans, B. T., Horvath, S., Ideker,T., Issa, J.-P. J., Kelsey, K. T., Marioni, R. E., Reik, W., Relton, C. L., Schalkwyk, L. C., Teschendorff, A. E., Wagner, W., ... Rakyan, V. K. (2019). DNA methylation aging clocks: challenges and recommendations. Genome Biology, 20(1), 249.
  3. Belsky, D. W., Caspi, A., Arseneault, L., Baccarelli, A., Corcoran, D. L., Gao, X., Hannon, E., Harrington, H. L., Rasmussen, L. J. H., Houts,R., Huffman, K., Kraus, W. E., Kwon, D., Mill, J., Pieper, C. F., Prinz, J. A., Poulton, R., Schwartz, J., Sugden, K., ... Moffitt, T. E. (2020). Quantification of the pace of biological aging in humans through a blood test, the DunedinPACE DNA methylation algorithm. ELife, 9,e54870.
  4. Belsky, D. W., Huffman, K. M., Pieper, C. F., Shalev, I., & Kraus, W. E. (2018a). Change in the Rate of Biological Aging in Response to Caloric Restriction: CALERIE Biobank Analysis. The Journals of Gerontology: Series A, 73(1), 4–10.
  5. Belsky, D. W., Moffitt, T. E., Cohen, A. A., Corcoran, D. L., Levine, M. E., Prinz, J. A., Schaefer, J., Sugden, K., Williams, B., Poulton, R., & Caspi, A. (2018b). Eleven Telomere, Epigenetic Clock, and Biomarker-Composite Quantifications of Biological Aging: Do They Measure the Same Thing? American Journal of Epidemiology, 187(6), 1220–1230.
  6. Belsky, D. W., Caspi, A., Cohen, H. J., Kraus, W. E., Ramrakha, S., Poulton, R., & Moffitt, T. E. (2017). 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, 16(4),644–651.
  7. Belsky, D. W., Caspi, A., Houts, R., Cohen, H. J., Corcoran, D. L., Danese, A., Harrington, H., Israel, S., Levine, M. E., Schaefer, J. D.,Sugden, K., Williams, B., Yashin, A. I., Poulton, R., & Moffitt, T. E. (2015). Quantification of biological aging in young adults. Proceedings of the National Academy of Sciences, 112(30), E4104 LP-E4110.
  8. Campisi, J., Kapahi, P., Lithgow, G. J., Melov, S., Newman, J. C., & Verdin, E. (2019). From discoveries in ageing research to therapeutics for healthy ageing . Nature, 571(7764), 183–192.
  9. Racette, S. B., Das, S. K., Bhapkar, M., Hadley, E. C., Roberts, S. B., Ravussin, E., Pieper, C., DeLany, J. P., Kraus, W. E., Rochon, J., & Redman, L. M. (2011). Approaches for quantifying energy intake and %calorie restriction during calorie restriction interventions in humans: the multicenter CALERIE study. American Journal of Physiology-Endocrinology and Metabolism, 302(4), E441–E448.
  10. Ravussin, E., Redman, L. M., Rochon, J., Das, S. K., Fontana, L., Kraus, W. E., Romashkan, S., Williamson, D. A., Meydani, S. N., Villareal,D. T., Smith, S. R., Stein, R. I., Scott, T. M., Stewart, T. M., Saltzman, E., Klein, S., Bhapkar, M., Martin, C. K., Gilhooly, C. H., ... Kritchevsky,S. (2015). A 2-Year Randomized Controlled Trial of Human Caloric Restriction: Feasibility and Effects on Predictors of Health Span and Longevity. The Journals of Gerontology: Series A, 70(9), 1097–1104.
  11. Teague, S., Youssef, G. J., Macdonald, J. A., Sciberras, E., Shatte, A., Fuller-Tyszkiewicz, M., Greenwood, C., McIntosh, J., Olsson, C. A.,Hutchinson, D., & SEED Lifecourse Sciences Theme (2018). Retention strategies in longitudinal cohort studies: a systematic review and meta-analysis . BMC medical research methodology, 18(1), 151.

Supplemental Information Selected by Vishal

  1. More About The DunedinPACE Study and Its Development - Rejuvenation Olympics.


OMICmAge - based on genomic, epigenomic, transcriptomic, proteomic, metabolomic, and phenomic measures



SystemAge - based on epigenetic markers in 9 (up to 11) heterogeneous organ systems

Most Recent Result

SystemAge - overview & pace of aging

SystemAge - organ system breakdown

Longitudinal Results

Only one measurement tracked so far; therefore longitudinal results are not available.

Description

Aging isn't one thing. Index, a revolutionary at-home biological age test developed by Elysium Health, now measures 10 different aspects of aging using the latest generation technology in the science of epigenetics. With Index you'll receive:

Source: Elysium Health

References

  1. Sehgal R., Meer, M., Shadyab, A.H., Casanova, R., Manson, J.E., Bhatti, P., Crimmins, E.M., Assimes, T.L., Whitsel, E.A., Higgins-Chen, A.T., Levine, M.E. Systems Age: A single blood methylation test to quantify aging heterogeneity across 11 physiological systems bioRxiv 2023.07.13.548904; doi: https://doi.org/10.1101/2023.07.13.548904
  2. Higgins-Chen AT, Thrush KL, Wang Y, Minteer CJ, Kuo PL, Wang M, Niimi P, Sturm G, Lin J, Moore AZ, Bandinelli S, Vinkers CH, Vermetten E, Rutten BPF, Geuze E, Okhuijsen-Pfeifer C, van der Horst MZ, Schreiter S, Gutwinski S, Luykx JJ, Picard M, Ferrucci L, Crimmins EM, Boks MP, Hägg S, Hu-Seliger TT, Levine ME. A computational solution for bolstering reliability of epigenetic clocks: Implications for clinical trials and longitudinal tracking. Nat Aging. 2022 Jul;2(7):644-661. doi: 10.1038/s43587-022-00248-2. Epub 2022 Jul 15. PMID: 36277076; PMCID: PMC9586209.



ActualAge - based on a broad spectrum of several hundred clinical biomarkers: "How old am I actually?"

Most Recent Result

This aging clock is still in development; therefore most recent result is not available.

Longitudinal Results

This aging clock is still in development; therefore longitudinal results are not available.

Description

There have been numerous valiant efforts to quantify biological age. However, with the advanced testing modalities now available, none of these aging clocks have hitherto comprehensively captured the deep nuances of individual physiology. OptiHuman aims to accomplish this very thing. Stay tuned.

Source: OptiHuman, Inc.

References

Supplemental Information Selected by Vishal

  1. Biomarkers of human mortality - A compilation of 471 unique biomarkers having statistically measurable effect sizes by BioAge Labs.


ApparentAge - based on computer vision algorithms on hi-res images: "How old do I look?"

Most Recent Result

This aging clock is still in its planning stages; therefore most recent result is not available.

Longitudinal Results

This aging clock is still in its planning stages; therefore longitudinal results are not available.

Description

If you were given 8 photographs of the same person at ages 18, 28, 38, 48, 58, 68, 78, and 88, but without the age label, would you be able to sort these 8 photographs in chronological order, effectively labeling the photographs with the age? I'm pretty sure you could.

Let's make this problem a little more challenging. What about 8 different people? You probably could still accurately label the 8 photoraphs if the 8 people were pretty "average" people. By "average", I mean none of these 8 people have significant outlier traits like the 18 year old having white hair or the 78 year old having access to Hollywood elite-level plastic surgery.

How are you able to do this? Well, our brains our wired to pick up small nuances and geometric details and average these perceptions into a composite understanding. Fine lines under the eye, uneven skin tone, crow's feet, laugh lines, degraded collagen fibers are all things we can detect unconsciously and use to determine an age estimate. Unless we've been living in a cave all our lives, we've trained our brains through tens of thousands or even hundreds of thousand data points where each data point is a face we've seen in person, on television, in magazines, or on the internet.

If you or I can differentiate accurately between faces 10 years apart, state of the art artificial intelligence should surely be able to resolve these differentials within a single year apart or even months apart. With advanced computer vision algorithms and sophisticated deep learning techniques, we should be able to similarly train a computer on hundreds of millions faces to be even better than we are with looking at a face and coming up with a number. At OptiHuman, this number is what we call the ApparentAge - essentially answering the question, "How old do I look?".

Source: OptiHuman, Inc.

References

Supplemental Information Selected by Vishal