Telomeres are the protective caps at the ends of eukaryotic chromosomes. Each time a somatic cell divides, a portion of the telomeric DNA fails to replicate; thus, telomeres naturally shorten with mitosis.1,2 Telomerase, the cellular enzyme that can counteract shortening by elongating and protecting telomeres, is kept at low levels in normal human cells.3 When telomeres become too short, cells lose the ability to grow and divide.4,5 Human genetic mutations that result in short telomeres are associated with a group of conditions collectively called “telomere syndromes” that resemble premature onset of diseases of aging,6 consistent with a role of telomere shortening in human aging in the general population.7
Recent studies have found that shorter leukocyte telomere length is associated with numerous age-related diseases, including cardiovascular disease,8–11 type 2 diabetes,12,13 dementia,14–16 and cancer,17,18 independent of chronological age. Although a number of studies have also reported that shorter telomeres are associated with increased mortality,17,19–30 others have failed to find an association between telomere length and survival31–34 (see the Appendix Table for a summary of findings). In general, studies that have not found an association between telomere length and mortality have examined older populations. Inconsistencies in the literature may be due to differences in sample size and measurement techniques, the use of population versus clinical samples, or unknown biologic differences between those who survive to extreme old age and those with earlier mortality.35,36
Using data from the National Health and Nutrition Examination Survey (NHANES) 1999–2002, this study examined the association between leukocyte telomere length and mortality in a large, nationally representative, socioeconomically and ethnically diverse sample of US adults. This study addressed the following research questions: (1) Is telomere length associated with all-cause mortality in adults ages 50–84 years? (2) Is telomere length associated with cause-specific mortality? (3) Do associations between telomere length and mortality vary according to age, sex, race/ethnicity, or socioeconomic status (SES)? Although prior research has shown that older age, male sex, white race, and low SES are associated with shorter telomere length,37,38 it is not known whether the relation between telomere length and mortality differs according to sociodemographic characteristics. Answering this question could help determine whether telomere length is a comparable indicator of mortality risk across population subgroups.
Sample and Procedures
Since 1960, the National Center for Health Statistics of the Centers for Disease Control and Prevention (CDC) has conducted NHANES to provide national estimates of the health and nutritional status of the US civilian noninstitutionalized population. NHANES 1999–2002 is a cross- sectional, nationally representative sample of more than 20,000 individuals ages 2 months and older. NHANES 1999–2002 utilized a four-stage sampling design: (1) primary sampling units (PSUs) consisting primarily of single counties, (2) area segments within PSUs, (3) households within segment areas, and (4) persons within households. On average, two to three individuals per household were sampled. NHANES 1999–2002 oversampled low-income persons, persons ages 12–19 years , persons ages 60 years and over, African-Americans, and Mexican-Americans to obtain more accurate estimates in these populations.
All NHANES 1999–2002 respondents ages 20 years and over were asked to provide DNA samples. Of the 10,291 respondents who were eligible to provide samples of DNA, 7,825 (76%) provided DNA and consented specifically to future genetic research. We excluded 653 respondents whose self-reported race/ethnicity was “other” or “other Hispanic,” because a goal of this study was to examine race/ethnic differences in the association between telomere length and mortality, and these groups are too diverse for our purposes. Given the possibility of survival bias among the extreme elderly,36 we also excluded 225 respondents ages 85+ years. Next, we excluded 3,747 respondents under age 50 years because we were interested in mortality due to chronic age-related diseases, which are rare among individuals under 50 years of age. Finally, we excluded an additional 109 respondents from the analytic sample due to missing data on one or more covariates (final n = 3,091). Human subjects approval for this study was provided by the Institutional Review Board at the CDC.
NHANES 1999–2002 survey data have been linked to death certificate data from the National Death Index, which uses a well-documented and validated method of matching deaths to population data sets.39,40 Codes for the underlying cause of death have been validated to have a discrepancy rate of approximately 5%.41 Mortality follow-up data are available from the date of survey participation until December 31, 2011. Person-years of follow-up from the interview date ranged from 0.08 to 12.7, with a mean of 9.5. We examined all-cause mortality, as well as mortality due to cardiovascular disease, cancer, and other diseases (including infectious disease, cerebrovascular disease, diabetes, Parkinson’s disease, Alzheimer’s disease, liver disease, and kidney disease). World Health Organization International Classification of Diseases, 10th Revision codes for the underlying causes of death included in these categories can be found in the eAppendix in the electronic version of the journal (https://links.lww.com/EDE/A912).
Telomere Length Assay
Aliquots of purified DNA were provided by the laboratory at the Division of Health and Nutrition Examination Surveys, National Center for Health Statistics, CDC. Using standardized procedures, DNA was extracted from whole blood and stored at −80°C. The telomere length assay was performed in the laboratory of Dr. Elizabeth Blackburn at the University of California, San Francisco, using the quantitative polymerase chain reaction (PCR) method to measure telomere length relative to standard reference DNA (T/S ratio), as described in detail elsewhere.42,43 Each sample was assayed three times on three different days. The samples were assayed on duplicate wells, resulting in six data points. Sample plates were assayed in groups of three plates, and no two plates were grouped together more than once. Each assay plate contained 96 control wells. Any assay runs with eight or more invalid control wells were considered a failed run and were excluded from further analysis (>99% of runs passed this criterion). The mean of the T/S ratio values was calculated, and the largest or the smallest T/S ratio value in the set (whichever deviated most from the mean) was marked as a potential outlier. Then the mean of the T/S ratio value was calculated without the potential outlier. If the absolute value of the log of the ratio between the recalculated mean (excluding the potential outlier) to the value of the potential outlier was greater than 0.4, then the value was marked as an outlier (98.7% of all samples contained no outliers).
The conversion from T/S ratio to kilobase pairs (kb pairs) was calculated based on comparison of telomeric restriction fragment length from Southern blot analysis and T/S ratios using DNA samples from the human diploid fibroblast cell line IMR90 at different population doublings. The formula to convert T/S ratio to kb pairs was (3,274 + 2,413 * (T/S))/1,000. DNA samples were coded and the lab was blinded to all other measurements in the study. The CDC conducted a quality control review before linking the telomere data to the NHANES 1999–2002 public use data files. The quality control protocol is available at http://www.cdc.gov/nchs/data/nhanes/genetics/Quality_Control_Public.pdf.
All models included controls for the following potential confounders: age (in years), sex (female = 1; male = 0), race/ethnicity (dummy variables for African-American and Mexican-American, with white as the reference category), education (dummy variables for less than high school, high school, and some college, with college degree as the reference category), smoking (dummy variables for current smoker and former smoker, with never smoker as the reference category), and body mass index (the ratio of weight/height2 in kg/m2). All models also included an age-squared term to account for potential nonlinearity in the association between age and mortality.
It is not clear whether chronic health conditions are a predictor or an outcome of reduced telomere length, or both. If they are, in fact, a predictor of shorter telomeres, then chronic conditions could potentially confound the association between telomere length and mortality. For this reason, we examined models with and without controls for the following chronic health conditions: cardiovascular disease (participant reported being told by their physician that they ever had a heart attack or stroke, or participant reported being told by their physician that they had coronary heart disease = 1; no heart attack, stroke, or coronary heart disease = 0), diabetes (participant reported physician diagnosis of diabetes or participant reported current use of medication for diabetes = 1; no diabetes or diabetes medication = 0), hypertension (participant reported being told by their physician that they had high blood pressure, or participant reported current use of medications to treat high blood pressure, or average measured blood pressure [the average of all measurements taken after dropping the first measurement] was 140 mmHg systolic or 90 mmHg diastolic = 1; no high blood pressure or blood pressure medication = 0), and high cholesterol (participant reported physician diagnosis of high cholesterol, or participant reported current use of medication to treat high cholesterol, or participant had a total cholesterol level of 240 mg/dl or higher = 1; no high cholesterol or cholesterol medication = 0).
We used Cox proportional hazards regression to estimate the risk of all-cause and cause-specific mortality. Censoring was done at the date of death or December 31, 2011. For the analysis of cause-specific mortality, deaths due to causes other than the cause of interest were treated as censored at the date of death. The time-scale was time-on-study. We verified the proportionality assumption by testing a model for each mortality outcome that included interactions of all the predictors with time. We examined models with telomere length as a continuous predictor and as a categorical predictor (dummy variables for the first, second, and third quartiles of telomere length, with the fourth quartile as the reference category). In these models, the continuous telomere length measure was multiplied by −1 to reflect increased risk of mortality associated with shorter telomeres. To determine whether the association between telomere length and mortality was moderated by age, sex, race/ethnicity, or SES, we also examined interactions between age and telomere length, sex and telomere length, race/ethnicity and telomere length, and education and telomere length. All regression models accommodated the complex sampling design of NHANES by incorporating strata and PSU indicators, as well as sample weights for the genetic subsample.44 The use of sample weights, which account for oversampling and nonresponse bias, helps ensure that estimates are representative of the general US population. Analyses were conducted on-site at the CDC Research Data Center in Atlanta and remotely using ANDRE, the CDC’s remote access system for the analysis of restricted data.
Table 1 shows mean leukocyte telomere length by selected characteristics of study participants. Age was inversely associated with telomere length; men had shorter telomeres than women; African Americans had longer telomeres than whites and Mexican Americans; and education was positively associated with telomere length. Telomere length did not vary by smoking status or body mass index. Telomere length was shorter by 13.5 base pairs per year of age; and approximately 6% of the variation in telomere length was explained by age (R2=0.06; results not tabulated).
A total of 870 deaths occurred during the follow-up period. The results of the Cox proportional hazards models for all-cause mortality, cardiovascular mortality, cancer mortality, and other disease mortality are shown in Table 2. In the full sample, ages 50–84 years, the continuous measure of telomere length was not associated with cardiovascular mortality or cancer mortality; however, a decrease of 1 kb pair in telomere length at baseline was marginally associated with a 10% increased hazard of all-cause mortality (hazard ratio [HR]: 1.1, 95% confidence interval [CI]: 0.9, 1.4) and a 30% increased hazard of death due to diseases other than cardiovascular disease or cancer (HR: 1.3, 95% CI: 0.9, 1.9). Compared with respondents in the longest quartile of telomere length, those in the shortest quartile had a 20% increased hazard of all-cause mortality (HR: 1.2, 95% CI: 1.0, 1.5); those in the second quartile had a 20% increased hazard of all-cause mortality (HR: 1.2, 95% CI: 1.0, 1.5); and those in the third quartile had a 10% increased hazard of all-cause mortality (HR: 1.1, 95% CI: 0.9, 1.4). For cardiovascular mortality, those in the second (HR: 1.4, 95% CI: 0.9, 2.0) and third (HR: 1.5, 95% CI: 1.0, 2.3) quartiles of telomere length had an increased hazard of death compared with those in the longest quartile. Finally, compared with respondents in the longest quartile of telomere length, those in the shortest quartile had a 50% increased hazard of death due to diseases other than cardiovascular disease or cancer (HR: 1.5, 95% CI: 1.0, 2.1). Results were substantively equivalent regardless of the inclusion of controls for chronic conditions.
The next step in the analysis was to examine age, sex, race/ethnicity, and SES as potential moderators of the association between telomere length and mortality. For all-cause mortality, cancer mortality, and mortality due to diseases other than cardiovascular disease or cancer, there was no evidence of moderating effects of age, sex, race/ethnicity, or education; however, we found an interaction between race/ethnicity and telomere length for cardiovascular mortality (see Table S1; https://links.lww.com/EDE/A912). As shown in Table 3, neither measure of telomere length was associated with cardiovascular mortality among white or Mexican-American respondents. Among African Americans, a decrease of 1 kb pair in telomere length at baseline was associated with a two-fold increased hazard of death due to cardiovascular disease (HR: 2.0, 95% CI: 1.3, 3.1); and those in the second (HR: 2.8, 95% CI: 1.0, 7.4) and third (HR: 2.7, 95% CI: 1.0, 7.7) quartiles of telomere length had a nearly three-fold increased hazard of death compared with those in the longest quartile. We found no evidence of interactions between age and telomere length, sex and telomere length, or education and telomere length for cardiovascular mortality (see Table S1; https://links.lww.com/EDE/A912).
Although evidence suggests that telomere shortening contributes to aspects of human aging,7 previous studies have not always found an association between telomere length and mortality. Conflicting findings may reflect several problems that have plagued research on telomere epidemiology, including small sample sizes, survival bias among the extreme elderly, and the use of different methods of telomere measurement.35,36 This study, which used the well-validated and now widely accepted PCR technique to measure telomere length, examined the association between leukocyte telomere length and mortality over an average of 9.5 years of follow-up in a large, nationally representative sample of US adults ages 50–84 years. We found limited support for the hypothesis that shorter telomere length is associated with increased risk of death. In the full sample, there was some evidence that shorter telomere length was associated with increased risk of all-cause mortality, cardiovascular mortality, and death due to diseases other than cardiovascular disease or cancer. Among African-American but not White or Mexican-American participants, shorter telomere length at baseline was associated with increased risk of cardiovascular mortality.
Limitations and Directions for Future Research
Despite the strengths of this study, there were several limitations. First, the follow-up period was relatively short (an average of 9.5 years). As a result, we only observed 870 deaths in the sample, which limited the power to detect associations between telomere length and mortality, particularly for cause-specific mortality. This may explain why we failed to replicate the results of a recent study examining the association between telomere length and cancer mortality that used similar data (a large, nationally representative Danish sample) and method of telomere measurement (PCR) but had up to 20 years of follow-up.30 Given the small number of deaths attributable to causes other than cardiovascular disease or cancer, we were unable to examine deaths due to infectious disease, cerebrovascular disease, diabetes, Parkinson’s disease, Alzheimer’s disease, liver disease, and kidney disease in separate models. The CDC will continue to link data from the National Death Index to the NHANES 1999–2002 data, which will provide greater power in the future to examine the hypothesis that reduced telomere length is associated with specific causes of death. Additional mortality data will also provide greater power to detect possible moderation effects of age, sex, race/ethnicity, and education in the association between telomere length and all-cause and cause-specific mortality.
Next, the accuracy of the cause-specific mortality data from the National Death Index depends on the accuracy of the death certificate data submitted to the National Center for Health Statistics by state vital statistics offices, and previous research has found evidence of problems with the reliability and accuracy of cause-of-death statements.45 Another potential limitation of the cause-specific mortality analysis was the assumption that competing risks of death were independent of one another. This assumption is only valid if the probability of death from one cause (e.g., cancer) is not altered by the probability of death from another cause (e.g., cardiovascular disease).46 Cox proportional hazards regression does not account for competing risks of death and, therefore, may overestimate the risk of death from a specific cause. However, given the short follow-up period in this study, the difference between traditional survival analysis and the competing risk approach is likely to be minimal.47 After the CDC has linked additional mortality follow-up data, researchers should consider examining competing risk models to ensure that results for cause-specific mortality analyses are not biased.
Another limitation of this study was the use of telomere length data measured at only one point in time. To determine whether the rate of change in telomere length influences mortality risk, it will be necessary for future longitudinal studies to obtain repeated measures of telomere length. Finally, leukocytes are composed of many cell types, including neutrophils, eosinophils, basophils, lymphocytes, and monocytes. Within the same individual, telomere length in different cell types varies.42 Given that prior infection and other factors, such as chronic stress exposure, affect the percentage of each cell type within an individual,42 this could potentially bias estimates of the association between overall leukocyte telomere length (an average of telomere length across all leukocyte cell types) and mortality.
This study found that the association between telomere length and mortality differs by race/ethnicity and cause of death. Because we examined data from a large, nationally representative dataset, the results of this work are generalizable to US adults ages 50–84 years. The findings suggest that telomere length may be a more useful indicator of cardiovascular mortality risk for African-Americans compared with whites and Mexican-Americans. This finding will be particularly important if telomere length is used in clinical settings.
This research was funded by Grant R01AG033592-01A1 from the National Institute on Aging (Elissa Epel, PI). The authors would like to thank Carolyn Neal, PhD, and Ajay Yesupriya, MPH, for their assistance. Jue Lin, Elizabeth Blackburn, and Elissa Epel were co-founders of Telome Health.
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