Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Resource
  • Published:

Measurement and initial characterization of leukocyte telomere length in 474,074 participants in UK Biobank

Nature Aging volume 2, pages 170–179 (2022)Cite this article

Subjects

Abstract

Leukocyte telomere length (LTL) is a proposed marker of biological age. Here we report the measurement and initial characterization of LTL in 474,074 participants in UK Biobank. We confirm that older age and male sex associate with shorter LTL, with women on average ~7 years younger in ‘biological age’ than men. Compared to white Europeans, LTL is markedly longer in African and Chinese ancestries. Older paternal age at birth is associated with longer individual LTL. Higher white cell count is associated with shorter LTL, but proportions of white cell subtypes show weaker associations. Age, ethnicity, sex and white cell count explain ~5.5% of LTL variance. Using paired samples from 1,351 participants taken ~5 years apart, we estimate the within-individual variability in LTL and provide a correction factor for this. This resource provides opportunities to investigate determinants and biomedical consequences of variation in LTL.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: DNA sample workflow to derive the final dataset.
Fig. 2: Distribution of weekly average LTL across duration of the study.

Similar content being viewed by others

Data availability

Access to samples was made available through the UKB Resource under application no. 6077. As per the standard terms of UKB, all data for the telomere measurements were returned to UKB to be made available to other researchers. All source data used in this study, including all data related to the telomere measurements are accessible via application to UKB. Further information on registration to access the data can be found at http://www.ukbiobank.ac.uk/register-apply/. Information on telomere measurements can be viewed in the data showcase (https://biobank.ndph.ox.ac.uk/showcase/) under the following fields: 22190 (unadjusted), 22191 (adjusted), 22192 (adjusted and z-transformed) and 22194 (both time point measurements for the regression dilution bias experiment).

Code availability

LTL measurement data were added to a custom-built database application. The source code for this is available at https://github.com/LCBRU/telomere. No other custom code was used in this study.

References

  1. Berry, J. D. et al. Lifetime risks of cardiovascular diseases. New Engl. J. Med. 366, 321–329 (2012).

    Article  CAS  PubMed  Google Scholar 

  2. Samani, N. J. & van der Harst, P. Biological ageing and cardiovascular disease. Heart 94, 537–539 (2008).

    Article  CAS  PubMed  Google Scholar 

  3. Blasco, M. A. Telomeres and human disease: ageing, cancer and beyond. Nat. Rev. Genet. 6, 611–622 (2005).

    Article  CAS  PubMed  Google Scholar 

  4. Harley, C. B. et al. Telomeres shorten during ageing of human fibroblasts. Nature 345, 458–460 (1990).

    Article  CAS  PubMed  Google Scholar 

  5. Allsopp, R. C. et al. Telomere length predicts replicative capacity of human fibroblasts. PNAS 89, 10114–10118 (1992).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Demanelis, K. et al. Determinants of telomere length across human tissues. Science 369, eaaz6876 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  7. Broer, L. et al. Meta-analysis of telomere length in 19,713 subjects reveals high heritability, stronger maternal inheritance and a paternal age effect. Eur. J. Hum. Genet. 21, 1163–10 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Li, C. et al. Genome-wide association analysis in humans links nucleotide metabolism to leukocyte telomere length. Am. J. Hum. Genet. 106, 389–404 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Dorajoo, R. et al. Loci for human leukocyte telomere length in the Singaporean Chinese population and trans-ethnic genetic studies. Nat. Commun. 10, 2491 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  10. Kimura, M. et al. Offspring’s leukocyte telomere length, paternal age, and telomere elongation in sperm. PLoS Genet. 4, e37 (2008).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  11. De Meyer, T. et al. Paternal age at birth is an important determinant of offspring telomere length. Hum. Mol. Genet. 16, 3097–3102 (2007).

    Article  PubMed  CAS  Google Scholar 

  12. Hansen, M. E. B. et al. Shorter telomere length in Europeans than in Africans due to polygenetic adaptation. Hum. Mol. Genet. 25, 2324–2330 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Lynch, S. M. et al. Race, ethnicity, psychosocial factors, and telomere length in a multicenter setting. PLoS ONE 11, e0146723 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  14. Needham, B. L. et al. Socioeconomic status, health behavior, and leukocyte telomere length in the National Health and Nutrition Examination Survey, 1999-2002. Soc. Sci. Med. 85, 1–8 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  15. Richter, T. & von Zglinicki, T. A. Continuous correlation between oxidative stress and telomere shortening in fibroblasts. Exp. Gerontol. 42, 1039–1042 (2007).

    Article  CAS  PubMed  Google Scholar 

  16. Valdes, A. M. et al. Obesity, cigarette smoking, and telomere length in women. Lancet 366, 662–664 (2005).

    Article  CAS  PubMed  Google Scholar 

  17. Ornish, D. et al. Effect of comprehensive lifestyle changes on telomerase activity and telomere length in men with biopsy-proven low-risk prostate cancer: 5-year follow-up of a descriptive pilot study. Lancet Oncol. 14, 1112–1120 (2013).

    Article  CAS  PubMed  Google Scholar 

  18. Farzaneh-Far, R. et al. Association of marine omega-3 fatty acid levels with telomeric aging in patients with coronary heart disease. JAMA 303, 250–257 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Weischer, M., Bojesen, S. E. & Nordestgaard, B. G. Telomere shortening unrelated to smoking, body weight, physical activity, and alcohol intake: 4,576 general population individuals with repeat measurements 10 years apart. PLoS Genet. 10, e1004191 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  20. Benetos, A. et al. Tracking and fixed ranking of leukocyte telomere length across the adult life course. Aging Cell 12, 615–621 (2013).

    Article  CAS  PubMed  Google Scholar 

  21. Armanios, M. & Blackburn, E. H. The telomere syndromes. Nat. Rev. Genet. 13, 693–704 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Zhu, X. et al. The association between telomere length and cancer risk in population studies. Sci. Rep. 6, 22243 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Brouilette, S. W. et al. Telomere length, risk of coronary heart disease, and statin treatment in the West of Scotland Primary Prevention Study: a nested case-control study. Lancet 369, 107–114 (2007).

    Article  CAS  PubMed  Google Scholar 

  24. Forero, D. A. et al. Meta-analysis of telomere length in Alzheimer’s disease. J. Gerontol. A Biol. Sci. Med. Sci. 71, 1069–1073 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  25. Kuszel, L., Trzeciak, T., Richter, M. & Czarny-Ratajczak, M. Osteoarthritis and telomere shortening. J. Appl. Genet. 56, 169–176 (2015).

    Article  CAS  PubMed  Google Scholar 

  26. Rode, L., Bojesen, S. E., Weischer, M., Vestbo, J. & Nordestgaard, B. G. Short telomere length, lung function and chronic obstructive pulmonary disease in 46,396 individuals. Thorax 68, 429–435 (2013).

    Article  PubMed  Google Scholar 

  27. Rosner, B., Willett, W. & Spiegelman, D. Correction of logistic regression relative risk estimates and confidence intervals for systematic within‐person measurement error. Stat. Med. 8, 1051–1069 (1989).

    Article  CAS  PubMed  Google Scholar 

  28. Hutcheon, J. A., Chiolero, A. & Hanley, J. A. Random measurement error and regression dilution bias. Brit. Med. J. 340, c2289 (2010).

    Article  PubMed  Google Scholar 

  29. Sudlow, C. et al. UK Biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 12, e1001779 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  30. Bycroft, C. et al. The UK Biobank resource with deep phenotyping and genomic data. Nature 562, 203–209 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Rode, L., Nordestgaard, B. G., Weischer, M. & Bojesen, S. E. Increased body mass index, elevated C-reactive protein, and short telomere length. J. Clin. Endocrinol. Metab. 99, E1671–E1675 (2014).

    Article  CAS  PubMed  Google Scholar 

  32. Wang, J. et al. Association between telomere length and diabetes mellitus: a meta-analysis. J. Int. Med. Res. 44, 1156–1173 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  33. Valente, C. et al. Effect of physical activity and exercise on telomere length: systematic review with meta-analysis. J. Am. Geriatr. Soc. https://doi.org/10.1111/jgs.17334 (2021).

  34. Strandberg, T. E. et al. Association between alcohol consumption in healthy midlife and telomere length in older men. The Helsinki Businessmen Study. Eur. J. Epidemiol. 27, 815–822 (2012).

    Article  PubMed  Google Scholar 

  35. Sharma, S., Malarcher, A. M., Giles, W. H. & Myers, G. Racial, ethnic and socioeconomic disparities in the clustering of cardiovascular disease risk factors. Ethn. Dis. 14, 43–48 (2004).

    PubMed  Google Scholar 

  36. Codd, V. et al. Identification of seven loci affecting mean telomere length and their association with disease. Nat. Genet. 45, 422–427 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Bekaert, S. et al. Telomere length and cardiovascular risk factors in a middle‐aged population free of overt cardiovascular disease. Aging Cell 6, 639–647 (2007).

    Article  CAS  PubMed  Google Scholar 

  38. Dalgård, C. et al. Leukocyte telomere length dynamics in women and men: menopause vs age effects. Int. J. Epidemiol. 44, 1688–1695 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  39. Ghimire, S., Hill, C. V., Sy, F. S. & Rodriguez, R. Decline in telomere length by age and effect modification by gender, allostatic load and comorbidities in National Health and Nutrition Examination Survey (1999–2002). PLoS ONE 14, e0221690 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Rehkopf, D. H. et al. Leukocyte telomere length in relation to 17 biomarkers of cardiovascular disease risk: a cross-sectional study of US adults. PLoS Med. 13, e1002188 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  41. Neuner, B. et al. Telomere length is not related to established cardiovascular risk factors but does correlate with red and white blood cell counts in a German blood donor population. PLoS ONE 10, e0139308 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  42. Mazidi, M., Penson, P. & Banach, M. Association between telomere length and complete blood count in US adults. Arch. Med. Sci. 13, 601–605 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  43. Lin, Y. et al. Age-associated telomere attrition of lymphocytes in vivo is co-ordinated with changes in telomerase activity, composition of lymphocyte subsets and health conditions. Clin. Sci. 128, 367–377 (2015).

    Article  CAS  Google Scholar 

  44. Fry, A. et al. Comparison of sociodemographic and health-related characteristics of UK Biobank participants with those of the general population. Am. J. Epidemiol. 186, 1026–1034 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  45. Batty, D. G., Gale, C. R., Kivimäki, M., Deary, I. J. & Bell, S. Comparison of risk factor associations in UK Biobank against representative, general population based studies with conventional response rates: prospective cohort study and individual participant meta-analysis. Brit. Med. J. 368, m131 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  46. Welsh, S., Peakman, T., Sheard, S. & Almond, R. Comparison of DNA quantification methodology used in the DNA extraction protocol for the UK Biobank cohort. BMC Genomics. 18, 26 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  47. Cawthon, R. M. Telomere length measurement by a novel monochrome multiplex quantitative PCR method. Nucleic Acids Res. 37, e21(2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

Download references

Acknowledgements

This research has been conducted using the UKB Resource under application no. 6077 and was funded by the UK Medical Research Council (MRC), Biotechnology and Biological Sciences Research Council and British Heart Foundation (BHF) through MRC grant MR/M012816/1 (V.C., C.P.N., J.R.T., J.N.D. and N.J.S.). The authors are also supported by grants from the BHF, SP/16/4/32697 (C.P.N.), RG/13/13/30194, RG/18/13/33946, CH/12/2/29428 (E.A., S.K., T.J., E.D.A., A.M.W., A.S.B. and J.N.D.); MRC, MR/L003120/1 (E.A., S.K., T.J., E.D.A., A.M.W., A.S.B. and J.N.D.); the National Institute for Health Research (NIHR) Leicester Cardiovascular Biomedical Research Centre (BRC-1215–20010, V.C., C.A.B., C.M., V.B., Q.W., S.E.H., C.P.N. and N.J.S.), NIHR Cambridge Biomedical Research Centre (BRC-1215-20014, E.A., S.K., T.J., E.D.A., A.M.W., A.S.B. and J.N.D.), NIHR Blood and Transplant Research Unit in Donor Health and Genomics (NIHR BTRU-2014-10024, E.A., S.K., T.J., E.D.A., A.M.W., A.S.B. and J.N.D.), Health Data Research UK (E.A., S.K., T.J., E.D.A., A.M.W., A.S.B. and J.N.D.) and EU/EFPIA Innovative Medicines Initiative Joint Undertaking BigData@Heart (11607, A.M.W. and E.A.). J.N.D. holds a BHF Personal Professorship. V.C., Q.W. and C.P.N. acknowledge support from the van Geest Heart and Cardiovascular Diseases Research Fund, University of Leicester. P. Akbari, T. Bolton and M. Arnold made computational and biostatistical contributions to this work. We thank L. Courtney, S. Welsh and D. Fry for assistance with UKB samples.

Author information

Authors and Affiliations

  1. Department of Cardiovascular Sciences, University of Leicester, Leicester, UK

    V. Codd, M. Denniff, C. Swinfield, S. C. Warner, M. Papakonstantinou, S. Sheth, D. E. Nanus, C. A. Budgeon, C. Musicha, V. Bountziouka, Q. Wang, R. Bramley, S. Stoma, C. P. Nelson & N. J. Samani

  2. NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Leicester, UK

    V. Codd, C. A. Budgeon, C. Musicha, V. Bountziouka, Q. Wang, R. Bramley, C. P. Nelson & N. J. Samani

  3. School of Population and Global Health, University of Western Australia, Crawley, Western Australia, Australia

    C. A. Budgeon

  4. British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK

    E. Allara, S. Kaptoge, T. Jiang, A. S. Butterworth, A. M. Wood, E. Di Angelantonio & J. N. Danesh

  5. National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, UK

    E. Allara, S. Kaptoge, A. S. Butterworth, A. M. Wood, E. Di Angelantonio & J. N. Danesh

  6. British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK

    S. Kaptoge, A. S. Butterworth, A. M. Wood, E. Di Angelantonio & J. N. Danesh

  7. Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK

    A. S. Butterworth, A. M. Wood, E. Di Angelantonio & J. N. Danesh

  8. Medical Research Council Biostatistics Unit, Cambridge Institute of Public Health, University of Cambridge, Cambridge, UK

    A. M. Wood

  9. The Alan Turing Institute, London, UK

    A. M. Wood

  10. Health Data Science Centre, Human Technopole, Milan, Italy

    E. Di Angelantonio

  11. Department of Health Sciences, University of Leicester, Leicester, UK

    J. R. Thompson

  12. Department of Human Genetics, Wellcome Sanger Institute, Hinxton, UK

    J. N. Danesh

Authors
  1. V. Codd
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. M. Denniff
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. C. Swinfield
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. S. C. Warner
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. M. Papakonstantinou
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. S. Sheth
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. D. E. Nanus
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. C. A. Budgeon
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. C. Musicha
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. V. Bountziouka
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Q. Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. R. Bramley
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. E. Allara
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. S. Kaptoge
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. S. Stoma
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. T. Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. A. S. Butterworth
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. A. M. Wood
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. E. Di Angelantonio
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. J. R. Thompson
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. J. N. Danesh
    View author publications

    You can also search for this author in PubMed Google Scholar

  22. C. P. Nelson
    View author publications

    You can also search for this author in PubMed Google Scholar

  23. N. J. Samani
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

M.D., C.S., M.P., S. Sheth, D.E.N. and V.C. generated the data. S.C.W., C.A.B., R.B., J.R.T., V.C. and C.P.N. curated the data. C.M., V.B., Q.W., A.S.B., J.R.T., V.C. and C.P.N. performed statistical analyses. V.C., C.P.N., C.M., Q.W., C.A.B., E.A., S.K., S. Stoma, V.B., T.J., E.D.A., A.M.W., A.S.B., J.R.T., J.N.D. and N.J.S. drafted the manuscript and all authors revised it. V.C., C.P.N., J.R.T., J.N.D. and N.J.S. (Principal Investigator) secured funding and oversaw the project.

Corresponding authors

Correspondence to V. Codd or N. J. Samani.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Aging thanks Timothy Hohman, Immaculata De Vivo, Sara Hagg and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Significant technical parameters affecting LTL measurements based on the stage 1 adjustment.

Summary box plots are shown for the 474, 074 LTL measurements for each associated parameter: Enzyme batch (A), PCR machine (B), primer batch (C), operator (D). Individual data points show minimum and maximum measures, the box represents the lower quartile (bottom), upper quartile (top) and median (internal line). The upper and lower whiskers extend to a value no further than 1.5 * IQR from the respective quartile. Linear relationships were seen between LTL and temperature (E) and humidity (F). For both (E) and (F) a fitted regression line is shown with 95% confidence intervals (gray shading).

Extended Data Fig. 2 Significant interactions based on the stage 2 adjustment.

(A) LTL by Primer and Operator. (B) LTL by primer and PCR machine. PCR machines 5 and 6 were not used at the start of the pilot study (primer batch 1) and machines 7 and 8 were used from the end of the pilot stage (primer batch 3 onwards).

Extended Data Fig. 3 Effect of A260/280 on LTL.

The distribution of DNA sample A260/280 ratios is illustrated in (A). We observed an increase in LTL with very low and very high A260/280 ratios (B). Data shown is mean LTL (blue0 with 95% confidence interval (gray).

Extended Data Fig. 4 Distribution of the coefficients of variation for the repeat samples.

Distribution of CVs after technical adjustment for both the blinded repeats (A) (n=528) and deliberate repeats (B) (n=22,615) are shown. The gray dotted line represents the median coefficient of variation with the shaded region representing the interquartile range.

Extended Data Fig. 5 Data on the first and second DNA sample used to estimate regression dilution ratio.

(A) Histogram showing that the gap between the two sample collections has a mean interval of 5.5 years (range: 2–10 years, N=1,312). (B) Correlation between the first and second loge-LTL measure by time, estimated by the difference in years between the two sample collections and shown with 95% confidence intervals. The blue circle reflects the correlation estimate (center) with size reflecting the number of participants measured each year (exact N shown in brackets). The black line shows the overall pooled correlation for all samples and the red dotted lines indicate the 95% confidence interval for this estimate.

Extended Data Fig. 6 Age and sex relationships for participants used to estimate regression dilution bias.

The decline of LTL with age is shown for men (blue) and women (plum) for both the first (A) and second (B) LTL measurements. The estimated effect sizes are shown for age (β_Age) and sex (β_Sex) within the figures.

Extended Data Fi. 7 Decline of LTL with age.

The decline of z-standardized loge-LTL with age is shown for men (blue) and women (plum) in adjusted data. The y-axis is truncated at -5SD to +5 SD with 166 data points (80 women, 86 men) not shown. A small number of participants recruited by UK Biobank fall outside of the stated 40–69 age range.

Extended Data Fig. 8 Decline in LTL with age by sex.

Using stratified regression for men (blue) and women (plum) for all participants (N=474,074) we considered the non-linear effect of age within each sex. Here we show the predicted shape in a solid line and the observed data in a dashed line with 95% confidence intervals. There is significant non-linearity observed for women, where the rate of LTL decline increases as the population ages.

Extended Data Fig. 9 Telomere lengths within individual ethnic groups.

Data adjusted for both age and sex are shown in purple for individual observations to indicate the range and quantity of data alongside a box-plot to show the median (line) and interquartile range (box) with whiskers extending to a value no further than 1.5 * IQR from the respective quartile. Box plots of data with additional adjustment for BMI, CRP, HbA1c, smoking status, alcohol consumption and measures of physical activity, socioeconomic status and diet are shown in blue. Ethnicity is self-reported and presented as defined by UK Biobank Data-Field 21000. Note that we shorten “Asian or Asian British” to Asian and “Black or Black British” to Black.

Extended Data Fig. 10 LTL by age in different ethnic groups.

LTL measurements were adjusted for sex, BMI, CRP, HbA1c, smoking status, alcohol consumption and measures of physical activity, socioeconomic status and diet.

Supplementary information

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Codd, V., Denniff, M., Swinfield, C. et al. Measurement and initial characterization of leukocyte telomere length in 474,074 participants in UK Biobank. Nat Aging 2, 170–179 (2022). https://doi.org/10.1038/s43587-021-00166-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s43587-021-00166-9

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing