Pace of Biological Aging

Overview:  Pace of biological aging refers to the rate at which individual experiences aging-related decline in system integrity, a cause of increased vulnerability to the development of chronic disease, the onset of disability, and mortality. A variety of methods are proposed to quantify the pace of biological aging and no gold-standard measurement exists. The most robust methods currently available utilize algorithms that combine information from multiple biomarkers of organ-system integrity or from DNA methylation marks at dozens or hundreds of sites across the genome. 


Background: Aging can be understood as a biological process, the gradual and progressive decline in system integrity that occurs with advancing chronological age (Kirkwood, 2005). This biological process proceeds faster in some individuals than others, leading to earlier onset of chronic disease, functional decline, and death (Kennedy et al., 2014). Individual differences in the pace of biological aging are thought to arise, in part, from processes of wear and tear that disrupt and damage biological systems, progressively impairing capacity to restore homeostasis following insults to system integrity. Stress-biology research suggests mechanisms through which experiences of psychological stress contribute to this wear-and-tear through chronic activation of the hypothalamic-pituitary-adrenal axis and sympathetic nervous system and related metabolic and immune dysregulation (Chrousos, 1995; Epel and Lithgow, 2014; Goldstein and McEwen, 2002; McEwen, 1998; Picard et al., 2017). Several recent discoveries suggest opportunities to investigate accelerated biological aging as a mediating link between stress exposures and aging-related disease, disability, and mortality. In particular, the pace of biological aging is already variable by young adulthood (Belsky et al., 2015) and may be accelerated by stressful experiences even earlier in life (Colich et al., 2019); biological aging proceeds more rapidly in individuals with a range of stress-linked risk factors, including early-life socioeconomic disadvantage and adverse childhood experiences (Belsky et al., 2017a, 2020); and biological aging appears to mediate the black-white health disparity in mortality risk, which is understood to reflect lifetime accumulation of stress exposure (Levine and Crimmins, 2014). Importantly, biological aging is modifiable through intervention (Belsky et al., 2017b), suggesting opportunities to translate quantifications of biological aging into surrogate endpoints for interventions that aim to mitigate health risks associated with stress exposure.

Collection and measurement: Three important dimensions of methods to quantify the pace of biological aging are (i) the criterion for which the measure is developed, (ii) the biological level of analysis at which they are implemented, and (iii) the number of measurements included and the computational strategy used to combine information from multiple measurements.


Criterion: There is no gold-standard definition of biological aging. Therefore, methods to quantify the pace of biological aging are developed by first establishing a proxy criterion on which the measure is based. Four common proxies are dysregulation/abnormality, chronological age differences, mortality risk, and within-person change-over-time. Approaches using these proxy measures are described briefly below:

  • Dysregulation/abnormality: This approach measures biological aging as the extent of dysregulation or abnormality in biological systems. First, a “normal” or “healthy” threshold value or range is assumed or estimated for the included biomarkers, often based on clinical guidelines. Aging is then quantified as the extent of deviation from normal values/ranges across the set of biomarkers, for example the count of biomarkers with out-of-range values. Examples of aging measures developed from this criterion are allostatic load (Seeman et al., 2001) and homeostatic dysregulation (Cohen et al., 2013).        

  • Chronological age differences: This approach measures biological aging as chronological age-related differences in biomarkers. First, one or a series of models are estimated to relate each biomarker to chronological age in a reference dataset. A simple example is multivariate regression of chronological age on a panel of biomarkers. Next, this model is used to compose an algorithm that can be applied to biomarker data from research participants. The output of the algorithm is a measurement interpretable as the age at which a participant’s biology/physiology would be approximately normal in the reference sample. This measurement is referred to as “biological age.” To estimate the pace of biological aging, a participant’s biological age is differenced from their chronological age, either by subtraction or by regressing biological age on chronological age and calculating residual values. Examples of aging measures developed from this criterion are Klemera-Doubal method Biological Age (Klemera and Doubal, 2006; Levine, 2013) and some epigenetic clocks (Hannum et al., 2013; Horvath, 2013).

  • Mortality risk: This approach models biological aging as an age-related difference in mortality risk. The approach typically combines information from two analyses, both of which are implemented in a reference sample. One analysis estimates mortality hazards for a range of chronological ages from a univariate model (i.e. chronological age is the only predictor). A second analysis models the hazard of mortality as a function of age and a panel of biomarkers. This second analysis is used to form a mortality-risk-prediction algorithm. Next, the mortality-risk-prediction algorithm is applied to age and biomarker data from research participants. Finally, the predicted mortality risks are converted to biological age values using the results from the first, univariate model. The resulting biological age can be interpreted as the age at which the participant’s mortality risk would be approximately normal in the reference population. The biological age is converted to an estimate of the pace of biological aging by differencing it from the participant’s true chronological age, either by subtraction or by regressing biological age on chronological age and calculating residual values. Examples of aging measures developed from this criterion are the Phenotypic Age and Phenotypic Aging epigenetic clock (Levine et al., 2018).

  • Within-person change-over-time: This approach models the pace of biological aging from within-person change on a panel of biological measures. First, measures are standardized to have parallel scaling, for example, based on their means and standard deviations at the baseline measurement. Second, mixed-effects growth models are fitted to the biomarker data. These models are used to predict participant-specific slopes of change for each biomarker. The slopes are then composited within each participant to form a single measure referred to as “Pace of Aging” (Belsky et al., 2015). This approach is distinct from the other three in that it directly measures within-person change occurring with aging. In contrast, the other three methods infer a biological process of aging from the difference between older and younger people or healthy and sick people, etc. A further distinction is that within-person-change-over-time methods are the only ones that provide true measures of the rate of aging. In contrast, other methods infer an aging rate from differences in the amount of aging that has occurred up to a fixed point in time. Pace of Aging and related measures may, therefore, be more sensitive to modifications of the aging rate that occur proximate to the time of measurement.


Biological level of analysis: Biological aging is thought to arise from the accumulation of cellular-level changes that, in turn, affect physiology (organ-system integrity), and ultimately drive clinical manifestations of disease and disability (Kennedy et al., 2014; López-Otín et al., 2013). Quantifications of the pace of biological aging may, therefore, be operationalized at the cellular level (e.g. telomere length, DNA methylation, gene expression), the physiological level (indices computed from biomarkers measuring organ-system integrity such as HbA1C, albumin, creatinine), and the clinical level (e.g. frailty indices that count a number of clinical deficits) (Belsky et al., 2018; Ferrucci Luigi et al., 2018). Most current quantifications of the pace of biological aging are implemented within a single level of biological analysis. However, several include information from multiple levels. Multi-level measurements are an important future direction for methods development.


Number of measurements and method of combination: Although some methods to quantify the pace of biological aging rely on a single biomarker (e.g. telomere length is often studied as a single-marker index of biological aging in stress research (Rentscher et al., 2020)), most integrate information from multiple measurements. Multi-measure indices have the advantage of superior measurement reliability and may be better-able to capture the multiple dimensions of biological change that occur with aging. The specific measurements included in multi-measure indices can be selected using hypothesis-driven methods, e.g. by selecting biomarkers that represent integrity of different organ systems, or using hypothesis-free methods, including machine learning approaches such as regularized regression. Different methods combine information from multiple measurements in different ways. Some apply threshold values to the individual measurements and sum the number of measurements that exceed threshold values (e.g. allostatic load, frailty indices). Others weight contributions from individual measurements in accordance with their association with some criterion (Klemera-Doubal method Biological Age, epigenetic clocks). To avoid overfitting of measurements to data, thresholds and/or weights should be defined in a reference dataset that is distinct from the dataset being used to conduct hypothesis testing. 



The several methods to quantify biological aging each have their own strengths and limitations. Different methods produce results that are only weakly correlated with one another (Belsky et al., 2018). Therefore, research using quantifications of the pace of biological aging must be precise about which measures are being used and why. In cases where it is possible to implement multiple methods, such as when whole-genome DNA methylation data are being used, comparative analysis of different approaches can help clarify the consistency of results across methods. In addition, there are two key limitations related to the populations in which most measures have been developed and validated. First, most measures have been developed in samples of midlife and older adults of European ancestry. In parallel, validation studies have also focused on this population. Therefore, the interpretation of biological aging measures in younger people remains uncertain, as does the validity of measurements across populations of different ancestries and those living outside of wealthy Western nations. Key frontiers in biological aging measurement development are studies validating measures at different life-course stages and in different population groups.


Table 1. Selected multi-measure indices of biological aging organized by the criterion measure used in their development and the biological level of analysis at which they are implemented.


Authors and Reviewer(s): This entry was prepared by Dr. Daniel W Belsky, Gloria Hu, and Dayoon Kwon. Reviewed by Drs. Steve Horvath and Judith Carroll. Please direct suggestions and feedback to Dr. Belsky (



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., et al. (2015). Quantification of biological aging in young adults. Proc. Natl. Acad. Sci. U. S. A. 112, E4104-4110.

Belsky, D.W., Caspi, A., Cohen, H.J., Kraus, W.E., Ramrakha, S., Poulton, R., and Moffitt, T.E. (2017a). 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, 644–651.

Belsky, D.W., Huffman, K.M., Pieper, C.F., Shalev, I., and Kraus, W.E. (2017b). Change in the Rate of Biological Aging in Response to Caloric Restriction: CALERIE Biobank Analysis. J. Gerontol. Ser. A 73, 4–10.

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., et al. (2018). Eleven Telomere, Epigenetic Clock, and Biomarker-Composite Quantifications of Biological Aging: Do They Measure the Same Thing? Am. J. Epidemiol. 187, 1220–1230.

Belsky, D.W., Caspi, A., Arseneault, L., Baccarelli, A., Corcoran, D.L., Gao, X., Hannon, E., Harrington, H.L., Rasmussen, L.J., Houts, R., et al. (2020). Quantification of the pace of biological aging in humans through a blood test, the DunedinPoAm DNA methylation algorithm. ELife 9, e54870.

Chrousos, G.P. (1995). The hypothalamic–pituitary–adrenal axis and immune-mediated inflammation. N. Engl. J. Med. 332, 1351–1363.

Cohen, A.A., Milot, E., Yong, J., Seplaki, C.L., Fülöp, T., Bandeen-Roche, K., and Fried, L.P. (2013). A novel statistical approach shows evidence for multi-system physiological dysregulation during aging. Mech. Ageing Dev. 134, 110–117.

Colich, N.L., Rosen, M.L., Williams, E.S., and McLaughlin, K.A. (2019). Biological Aging in Childhood and Adolescence Following Experiences of Threat and Deprivation: A Systematic Review and Meta-Analysis. BioRxiv 642405.

Epel, E.S., and Lithgow, G.J. (2014). Stress Biology and Aging Mechanisms: Toward Understanding the Deep Connection Between Adaptation to Stress and Longevity. J. Gerontol. A. Biol. Sci. Med. Sci. 69, S10–S16.

Ferrucci Luigi, Levine Morgan E., Kuo Pei-Lun, and Simonsick Eleanor M. (2018). Time and the Metrics of Aging. Circ. Res. 123, 740–744.

Fried, L.P., Tangen, C.M., Walston, J., Newman, A.B., Hirsch, C., Gottdiener, J., Seeman, T., Tracy, R., Kop, W.J., Burke, G., et al. (2001). Frailty in Older Adults Evidence for a Phenotype. J. Gerontol. A. Biol. Sci. Med. Sci. 56, M146–M157.

Goldstein, D.S., and McEwen, B. (2002). Allostasis, Homeostats, and the Nature of Stress. Stress 5, 55–58.

Hannum, G., Guinney, J., Zhao, L., Zhang, L., Hughes, G., Sadda, S., Klotzle, B., Bibikova, M., Fan, J.-B., Gao, Y., et al. (2013). Genome-wide methylation profiles reveal quantitative views of human aging rates. Mol. Cell 49, 359–367.

Horvath, S. (2013). DNA methylation age of human tissues and cell types. Genome Biol. 14, R115.

Kennedy, B.K., Berger, S.L., Brunet, A., Campisi, J., Cuervo, A.M., Epel, E.S., Franceschi, C., Lithgow, G.J., Morimoto, R.I., Pessin, J.E., et al. (2014). Geroscience: linking aging to chronic disease. Cell 159, 709–713.

Kirkwood, T.B.L. (2005). Understanding the Odd Science of Aging. Cell 120, 437–447.

Klemera, P., and Doubal, S. (2006). A new approach to the concept and computation of biological age. Mech. Ageing Dev. 127, 240–248.

Levine, M.E. (2013). Modeling the rate of senescence: Can estimated biological age predict mortality more accurately than chronological age? J. Gerontol. A. Biol. Sci. Med. Sci. 68, 667–674.

Levine, M.E., and Crimmins, E.M. (2014). Evidence of accelerated aging among African Americans and its implications for mortality. Soc. Sci. Med. 1982 118, 27–32.

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., et al. (2018). An epigenetic biomarker of aging for lifespan and healthspan. Aging 10, 573–591.

Liu, Z., Kuo, P.-L., Horvath, S., Crimmins, E., Ferrucci, L., and Levine, M. (2018). A new aging measure captures morbidity and mortality risk across diverse subpopulations from NHANES IV: A cohort study. PLOS Med. 15, e1002718.

López-Otín, C., Blasco, M.A., Partridge, L., Serrano, M., and Kroemer, G. (2013). The hallmarks of aging. Cell 153, 1194–1217.

Lu, A.T., Quach, A., Wilson, J.G., Reiner, A.P., Aviv, A., Raj, K., Hou, L., Baccarelli, A.A., Li, Y., Stewart, J.D., et al. (2019). DNA methylation GrimAge strongly predicts lifespan and healthspan. Aging 11, 303–327.

McEwen, B.S. (1998). Protective and damaging effects of stress mediators. N. Engl. J. Med. 338, 171–179.

Picard, M., Juster, R.-P., Sloan, R., and McEwen, B. (2017). Mitochondrial Nexus to Allostatic Load Biomarkers. Psychosom. Med. 79, 114–117.

Rentscher, K.E., Carroll, J.E., and Mitchell, C. (2020). Psychosocial Stressors and Telomere Length: A Current Review of the Science. Annu. Rev. Public Health 41, 223–245.

Seeman, T.E., McEwen, B.S., Rowe, J.W., and Singer, B.H. (2001). Allostatic load as a marker of cumulative biological risk: MacArthur studies of successful aging. Proc. Natl. Acad. Sci. U. S. A. 98, 4770–4775.