Eleven Telomere, Epigenetic Clock, and Biomarker-Composite Quantifications of Biological Aging: Do They Measure the Same Thing?

Abstract

The geroscience hypothesis posits that therapies to slow biological processes of aging can prevent disease and extend healthy years of life. To test such "geroprotective" therapies in humans, outcome measures are needed that can assess extension of disease-free life span. This need has spurred development of different methods to quantify biological aging. But different methods have not been systematically compared in the same humans. We implemented 7 methods to quantify biological aging using repeated-measures physiological and genomic data in 964 middle-aged humans in the Dunedin Study (New Zealand; persons born 1972-1973). We studied 11 measures in total: telomere-length and erosion, 3 epigenetic-clocks and their ticking rates, and 3 biomarker-composites. Contrary to expectation, we found low agreement between different measures of biological aging. We next compared associations between biological aging measures and outcomes that geroprotective therapies seek to modify: physical functioning, cognitive decline, and subjective signs of aging, including aged facial appearance. The 71-cytosine-phosphate-guanine epigenetic clock and biomarker composites were consistently related to these aging-related outcomes. However, effect sizes were modest. Results suggested that various proposed approaches to quantifying biological aging may not measure the same aspects of the aging process. Further systematic evaluation and refinement of measures of biological aging is needed to furnish outcomes for geroprotector trials.

Figure 1.

Taxonomy of the biological aging measures for use in humans that are evaluated in this article. Epigenetic clocks are composed of dozens or hundreds of different methylation marks across the genome. We classify the clocks in the “single measure” row because genome-wide DNA methylation is measured in a single assay and reflects a single biological substrate.

Figure 2.

Distributions of cross-sectional biological aging measures and pace of aging in the Dunedin birth cohort at age 38 years (born during 1972–1973), New Zealand. Panels A through D plot biological ages estimated from DNA methylation and clinical biomarker data: A) 353–cytosine-phosphate-guanine (CpG) epigenetic clock; B) 99-CpG epigenetic clock; C) 71-CpG epigenetic clock; and D) Klemera-Doubal method (KDM) biological age algorithm. In these panels, the dashed gray line is set at age 38 years, the chronological age of the cohort at the time assays were taken. E) Telomere/single copy (T/S) ratio at chronological age 38 years. F) Age-related homeostatic dysregulation, also assayed at chronological age 38 years. G) Pace of aging, which was derived based on repeated measurements taken at ages 26, 32, and 38 years.

Figure 3.

Correlations among 7 measures of biological aging in a birth cohort at chronological age 38 years (born during 1972–1973), New Zealand. The figure shows a matrix of correlations illustrating relationships among 7 measures of biological aging: leukocyte telomere length; 353-, 99-, and 71–cytosine-phosphate-guanine (CpG) epigenetic clocks; Klemera-Doubal method (KDM) biological age; age-related homeostatic dysregulation; and pace of aging. Data are for n = 800 study members with complete data on all biological aging measures. Correlations are shown above the diagonal. Values reflect Pearson correlations between the variable listed to the left and the variable listed below. Correlations of ≥0.07 are statistically significant at P < 0.05. Correlations between aging measures computed with adjustment for sex differences are reported in Web Table 6.

Figure 4.

Associations of cross-sectional biological aging measures and pace of aging with health span–related characteristics in a birth cohort at chronological age 38 years (born during 1972–1973), New Zealand. The figure shows bar charts of effect sizes for each of the 7 measures of biological aging. Effect sizes were estimated for 4 measures of physical functioning (balance, grip strength, motor coordination, and self-reported physical limitations), cognitive functioning (intelligence-quotient score at age 38 years from the Wechsler Adult Intelligence Scale) and cognitive decline (change in Wechsler-scale intelligence-quotient score since childhood), and 2 measures of subjective aging (self-rated health and facial aging from assessments of facial photographs of the study member by independent raters). In the figure, groups of health span–related characteristics are denoted by different colors. Physical function measures are shown in dark blue. Cognitive measures are shown in light blue. Subjective aging measures are shown in red. Effect sizes for subtests of cognitive function and cognitive decline are graphed in Web Figure 2. Health span–related characteristics were scored so that higher values indicated increased health span. Telomere length was reversed for this analysis so that higher values corresponded to shorter telomeres. Thus, the expected direction of association for all effect sizes was negative—because faster biological aging is expected to shorten health span. Effect sizes are presented for the following measures: A) Telomere shortness; B) 353–cytosine-phosphate-guanine (CpG) clock; C) 99-CpG clock; D) 71-CpG clock; E) Klemera-Doubal (KDM) biological age; F) log age-related homeostatic dysregulation; and G) pace of aging.