Physical Activity, Physical Fitness, and Leukocyte Telomere Length: The Cardiovascular Health Study.

EPIDEMIOLOGY

Physical Activity, Physical Fitness, and Leukocyte Telomere Length: The Cardiovascular Health Study LUISA SOARES-MIRANDA1, FUMIAKI IMAMURA2, DAVID SISCOVICK3,4, NANCY SWORDS JENNY5, ANNETTE L. FITZPATRICK4, and DARIUSH MOZAFFARIAN6 1

Research Center in Physical Activity Health and Leisure, Faculty of Sport, University of Porto, Porto, PORTUGAL; 2MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge Biomedical Campus, Cambridge, UNITED KINGDOM; 3New York Academy of Medicine, New York, NY; 4Department of Epidemiology, University of Washington, Seattle, WA; 5Department of Pathology, University of Vermont College of Medicine, Burlington, VT; and 6Friedman School of Nutrition Science and Policy, Tufts University, Boston, MA ABSTRACT SOARES-MIRANDA, L., F. IMAMURA, D. SISCOVICK, N. S. JENNY, A. L. FITZPATRICK, and D. MOZAFFARIAN. Physical Activity, Physical Fitness, and Leukocyte Telomere Length: The Cardiovascular Health Study. Med. Sci. Sports Exerc., Vol. 47, No. 12, pp. 2525–2534, 2015. Introduction: The influence of physical activity (PA) and physical fitness (PF) at older ages on changes in telomere length (TL)—repetitive DNA sequences that may mark biologic aging—is not well-established. Few prior studies (mainly cross-sectional) have been conducted in older adults, and few studies have evaluated PF. Methods: We investigated cross-sectional and prospective associations of PA and PF with leukocyte TL among 582 older adults (mean T SD age, 73 T 5 yr at baseline) in the Cardiovascular Health Study, with serial TL measures and PA and PF assessed multiple times. Cross-sectional associations were assessed using multivariable repeated-measures regression, in which cumulatively averaged PA and PF measures were related to TL. Longitudinal analyses assessed cumulatively averaged PA and PF against later changes in TL, and changes in cumulatively averaged PA and PF against changes in TL. Results: Cross-sectionally, greater walking distance and chair test performance, but not other PA and PF measures, were each associated with longer TL (P trend = 0.007 and 0.04, respectively). In longitudinal analyses, no significant associations of baseline PA and PF with change in TL were observed. In contrast, changes in leisure-time activity and chair test performance were each inversely associated with changes in TL. Conclusions: Cross-sectional analyses suggest that greater PA and PF are associated with longer TL. Prospective analyses show that changes in PA and PF are associated with differences in changes in TL. Even later in life, changes in certain PA and PF measures are associated with changes in TL, suggesting that leisure-time activity and fitness could reduce leukocyte telomere attrition among older adults. Key Words: BIOLOGIC AGING, DNA, ELDERLY, EXERCISE, FITNESS

T

elomere length (TL)—repetitive DNA sequences at the ends of eukaryotic chromosomes that act as ‘‘caps,’’ protecting genomic integrity and stability

(46)—has received attention as a potential marker of biologic aging (4,20). Leukocyte TL in humans has been associated with age-related diseases, disease biomarkers, and mortality (4,5,9,11,12,30,32). For example, in the Cardiovascular Health Study, shorter TL was associated with higher risk of cardiovascular disease (CVD), age-related disease burden, and mortality (5,11,12,32). Shortening of TL may be predominantly influenced by oxidative stress and inflammation (33). It has been hypothesized that higher levels of physical activity (PA) and physical fitness (PF) may delay TL shortening, potentially through anti-inflammatory and antioxidative mechanisms (22,41). Greater PA and PF are consistently associated with lower morbidity and mortality from chronic diseases (1), supporting a potential ‘‘antiaging’’ effect. Yet, only limited epidemiologic evidence supports an influence on PA or PF on TL. Among prior studies, some observational studies

Address for correspondence: Luisa Soares-Miranda, Ph.D., Research Center in Physical Activity, Health, and Leisure, Faculty of Sport, University of Porto, Rua Dr. Pla´cido Costa, 91 4200.450 Porto, Portugal; E-mail: [email protected]. Submitted for publication January 2015. Accepted for publication May 2015. Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal_s Web site (www.acsm-msse.org). 0195-9131/15/4712-2525/0 MEDICINE & SCIENCE IN SPORTS & EXERCISEÒ Copyright Ó 2015 by the American College of Sports Medicine DOI: 10.1249/MSS.0000000000000720

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Copyright © 2015 by the American College of Sports Medicine. Unauthorized reproduction of this article is prohibited.

EPIDEMIOLOGY

(7,8,17,18,21,28,34,48) and one intervention study (26) suggested favorable roles for PA or PF in TL profiles, but other observational (6,47) and intervention (36,42) studies did not. In addition, all of the observational studies assessed only cross-sectional associations of a single PA or PF measure, limiting conclusions on long-term cumulative PA or PF. The two interventional studies were also of short duration (3–6 months), limiting inference on the effects of longterm PA or PF. No prior studies have separately measured PA and PF and assessed whether each of PA and PF was independently associated with TL. For example two of four prior studies had small samples sizes (N G 65), limiting statistical power for detecting associations; none separately evaluated PA and PF to determine their potential independent associations with TL. Finally, few of these prior studies were conducted in older adults (18,28,34,47), a population that is particularly relevant to the study of aging because old age is associated with a high prevalence of chronic diseases and, consequently, a possibly accelerated rate of telomere shortening. A recent 6-month randomized controlled PA trial in 68-yr-old sedentary and overweight subjects suggested that reduced sitting time, but not greater time spent exercising, was associated with telomere lengthening (38). However, this study had a small sample size (N = 49) (38). To address these issues and to determine whether longterm PA and PF are associated with TL and TL attrition later in life, we investigated the cross-sectional and prospective associations of PA and PF with TL in a community-based cohort study of older US adults.

METHODS Population. The design and recruitment process of the Cardiovascular Health Study have been described (13,44). Briefly, 5201 ambulatory noninstitutionalized men and women age 65 yr or older were randomly selected and enrolled from Medicare eligibility lists in four US communities in 1989–1990; an additional 687 Black participants were similarly recruited and enrolled in 1992. The institutional review committee at each center approved the study, and all participants provided a written informed consent form. From 1989–1990 to 1998–1999, participants were followed by annual study visits. Standardized evaluations included physical examination, diagnostic testing, laboratory evaluation, and questionnaires on health status, medical history, and cardiovascular risk factors (13,27,44). Blood was collected and stored during most visits, and DNA was collected from those participants who provided consent for the use of their genetic material. Individuals from each enrollment phase were included in the present study if they consented to the use of their DNA, had at least 12 mg of DNA available, had stored leukocytes for additional DNA preparation, and had measures of PA and PF at baseline. The characteristics of individuals included in this analysis were generally similar to those of the whole cohort.

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Official Journal of the American College of Sports Medicine

Assessment of PA and PF. PA was assessed at multiple serial visits (Figure, Supplemental Digital Content 1, Timeline, http://links.lww.com/MSS/A553). Usual leisuretime activity was assessed using a modified validated Minnesota Leisure-Time Activities questionnaire, which has been associated with risk of multiple disease outcomes in this cohort (23). The questionnaire evaluated the frequency and duration of 15 different activities during the prior 2 wk, including gardening, mowing, raking, swimming, hiking, aerobics, tennis, jogging, racquetball, walking, golfing, bicycling, dancing, calisthenics, and exercise cycling (37). Each activity was expressed in metabolic equivalents of task (43), and participant responses to the type, frequency, and duration of each activity were used to calculate weekly energy expenditure (in kilocalories per week) from leisure-time activity. Usual exercise intensity was also assessed (none, low, moderate, high) (37). Usual walking habits, including mean walking pace (gait speed) and distance walked, were assessed annually at each follow-up visit. We evaluated these metrics in prespecified categories, including the following: usual pace walked (G2, 2–3, 93 mph), blocks walked (quintiles), exercise intensity (none, low, moderate, high) and leisure-time activity (quintiles). A previously defined walking score was also evaluated based on the combination of walking pace and walking distance (23). PF was also assessed at multiple serial visits (Figure, Supplemental Digital Content 1, Timeline assessment of physical activity and physical fitness and telomere length in the Cardiovascular Health Study between 1989–1990 and 1997–1998, http://links.lww.com/MSS/A553) based on 15-ft walk (in seconds), grip strength (in kilograms), and chair stand (in seconds). In the 15-ft walk, a trained examiner measured the time needed for participants to walk a 15-ft course (4.5 m) at their usual pace. Grip strength in the dominant hand was measured using a hand-held JAMAR dynamometer, recording the force (in kilograms) for the best of three attempts at maximal squeeze. For chair stand, a trained examiner recorded how quickly each participant performed five consecutive chair stands (standing up, with arms folded across the chest, from a seated position on a 45-cm-tall chair), timed to the nearest tenth of 1 s. We evaluated each PF measure separately and, similar to the walking score, also constructed a summary measure based on all three PF measures (each in quintiles) to better capture the full variation of PF within the cohort. Measurement of TL. TL (in kilo base pairs) was measured as the mean length of terminal restriction fragments in peripheral leukocytes (4,11,25). Five hundred eighty-two older adults who consented to DNA preparation and use, had at least 12 Kg of available DNA, and had stored leukocytes for additional DNA preparation in both 1992–1993 and 1997–1998 were included in the present analysis of change in TL. TL was measured using Southern blot analysis, as previously described (3,25). Each sample was analyzed twice on different gels on different occasions, with the mean value used for statistical analyses. The Pearson correlation coefficient for

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PHYSICAL ACTIVITY AND TELOMERE LENGTH

were related to TL in 1992–1993, and cumulatively averaged PA and PF from 1993–1998 were related to TL in 1997–1998. PA measures were assessed as categorical (indicator) variables, with tests for trend evaluated by entering PA categories as ordinal variables. Longitudinal analyses of PA and PF with change in TL were assessed using multivariable linear regression. Cumulatively averaged PA and PF from 1989–1993 were related to the subsequent change in TL between 1992–1993 and 1997– 1998; changes in cumulatively averaged PA and PF between 1989–1993 and 1993–1998 were related to changes in TL between 1992–1993 and 1997–1998. The rate of change in TL was calculated (in base pairs per year) as: (TL1997–1998 j TL1992–1993)/follow-up years. To minimize confounding, we adjusted models for major demographic factors including age, sex, race, study enrollment site, education, income, smoking status, and usual dietary habits, including consumption of total energy, omega-3 polyunsaturated fatty acids, omega-6 polyunsaturated fatty acids, and dietary fiber (6,10). We also evaluated factors that could be plausible biologic intermediates (i.e., on the putative causal pathway between PA and TL), including, BMI, waist circumference, fasting glucose, insulin, inflammatory markers, and prevalent diseases including type 2 diabetes mellitus and CVD. In additional analyses, we evaluated both PA and PF measures in the same model to assess their independent associations with TL. To minimize the possibility of reverse causation (poor health causing low PA/PF), we performed sensitivity analyses restricted to participants reporting only good, very good, or excellent overall health and having no limitation in activities of daily living or instrumental activities. Because the measured change in TL was positive in some participants (45%; potentially representing measurement error given that TL is not generally expected to increase), we also performed sensitivity analyses evaluating change in TL as a binary variable (any attrition; yes, no) and as a continuous variable but with any observed increases recoded as 0 (no change). We assessed potential interaction by age, sex, race, and BMI by including a cross-product term of each potential modifier and each PA/PF measure in the regression model, evaluating significance of interaction using Wald test. Analyses were performed using Stata 10.0 (StataCorp, College Station, TX; two-tailed alpha = 0.05).

RESULTS At baseline, the mean T SD age was 73 T 5 yr, and 62% of participants were women (Table 1). About one in five participants had prevalent CHD, and one in seven participants had prevalent diabetes. Participants spent a mean T SD of 1045 T 1446 kcalIwkj1 on leisure-time activities, and 35% engaged in moderate-intensity PA. Participants walked a mean T SD of 41 T 65 blocks per week, with 67% having a pace faster than 2 mph. The mean T SD time needed to complete a distance of 15 ft and five chair stands was 5.5 T 2.0

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EPIDEMIOLOGY

these duplicates was 0.97 (mean coefficient of variation for pair sets, 1.5%). The laboratory conducting TL measurements was blinded to all participant characteristics. DNA integrity was assessed through electrophoresis of 0.5 Kg of DNA on 1.0 ethidium bromide. These measures suggested some degradation, which would attenuate the ability to detect differences in changes in TL across time, especially across only 5 yr (1992–1993 to 1997–1998). Covariates. Information on a wide range of covariates was obtained during study visits, including demographics, education, income, detailed smoking habits, alcohol use, usual dietary habits, body mass index (BMI), medication use, hypertension, diabetes, presence or absence of CHD, and presence or absence of congestive heart failure (13). BMI was calculated as weight (in kilograms)/height (in meters)2. Hypertension status was defined as either not present (systolic blood pressure G140 mm Hg, diastolic blood pressure G90 mm Hg, and no use of antihypertensive medication), borderline (systolic pressure of 140–159 mm Hg or diastolic pressure of 90–94 mm Hg and no use of antihypertensive medication), or definite (systolic pressure Q160 mm Hg, diastolic pressure Q95 mm Hg, or use of antihypertensive medication). Diabetes mellitus was classified using the criteria of the American Diabetes Association (21) (not present, impaired fasting glucose, definite diabetes). Myocardial infarction was diagnosed using an algorithm including cardiac symptoms (such as chest pain), abnormal cardiac enzyme concentrations, and serial electrocardiogram changes. Fatal CHD included deaths not meeting criteria for myocardial infarction but occurred within 72 h of chest pain or with a history of ischemic heart disease. CHD includes myocardial infarction, angina, angioplasty, bypass, and death due to atherosclerosis. Stroke was classified as ischemic (evidence of focal brain deficit without evidence of primary hemorrhage), hemorrhagic (bloody spinal fluid on lumbar puncture or evidence of blood in the subarachnoid space, ventricles, or parenchyma on brain imaging or at surgery or autopsy that did not appear consistent with hemorrhage into an infarction), or unknown (information was insufficient for classification) (19). CVD was defined as combined incidence of deaths due to stroke, fatal and nonfatal myocardial infarction, and CHD. Statistical analysis. Cross-sectional associations of PA and PF with TL were assessed using multivariable repeatedmeasures linear regression, utilizing measures of TL in both 1992–1993 and 1997–1998 and accounting for within-person correlation. To minimize misclassification (measurement error) and to better represent long-term effects of habitual PA and PF, we took advantage of repeated measures of PA to PF to perform cumulative updating (averaging of serial values) (Figure, Supplemental Digital Content 1, Timeline assessment of physical activity and physical fitness and telomere length in the Cardiovascular Health Study between 1989–1990 and 1997–1998, http://links.lww.com/MSS/A553). When PA or PF was missing, existing values were carried forward. Cumulatively averaged PA and PF measures from 1989–1993

TABLE 1. Baseline (1992–1993) characteristics of 582 older US adults in the Cardiovascular Health Study with longitudinal assessment of PA, PF, and TL. Characteristic Age, yr Sex: male Race: White Education Less than high school High school More than high school Annual income QUS$25,000 Smoking habits Former smoker Current smoker BMI, kgImj2 Prevalent CHD Prevalent congestive heart failure Prevalent diabetes mellitus PA Walking pace G2 mph Q2 mph Walking blocks, blocks per week Exercise intensity None Low Moderate High Leisure-time activity, kcalIwkj1 PF Walk test, s (for every 15 ft) Hand grip test, kg Chair test, s (for every five chair stands)

73 T 5 38 85 24 32 43 39 44 10 27 T 5 20 5 14

33 67 41 T 65 8 45 35 12 1045 T 1446 5.5 T 2.0 27.5 T 9.8 14.8 T 4.9

EPIDEMIOLOGY

Data are expressed as mean T SD (continuous variables) or percentage (categorical variables). CHD refers to a history of myocardial infarction, angina, or coronary revascularization. Congestive heart failure is defined by the presence of the following symptoms: sleeps on two pillows to breathe, awakened at night by difficulty breathing, and swelling of feet and ankles during the day that goes down overnight. Diabetes is defined as a fasting glucose level 9140 mgIdLj1, a glucose level 9200 mgIdLj1 at 2 h after oral challenge, or use of insulin or oral hypoglycemic medications.

and 14.8 T 4.9 s, respectively. In addition, the mean T SD hand grip strength was 27.5 T 9.8 kg. Overall at baseline, TL ranged from 5.1 to 8.6 kb, with a mean T SD of 6.3 T 0.6 kb and a median of 6.3 kb. The mean T SD change in TL (calculated as TL1997–1998 j TL1992–1993) was j0.012 T 0.18 kb between 1992–1993 and 1997–1998, for an annualized attrition of j2.44 bpIyrj1. Cross-sectional analysis of PA, PF, and TL. In crosssectional multivariable-adjusted analyses, greater reported walking distance and better chair test performance were associated with longer TL (P trend = 0.007 and 0.04, respectively) (Table 2). In addition, a better overall fitness score was associated with a trend toward longer TL (P trend = 0.09). In contrast, walking pace, leisure-time activity, time to complete a 15-ft walk, and hand grip strength were not significantly associated with TL. Analysis included only participants with excellent, very good, and good health status; those with no limitations in activities of daily living or instrumental activities generated similar results. Longitudinal analysis of PA and PF and change in TL. In multivariable longitudinal analyses, no significant associations between PA and PF from 1989–1993 and subsequent 5-yr change in TL were observed (Table 3). Results for participants with good or better health status and without limitations in activities of daily living or instrumental

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activities were generally similar. In secondary analyses evaluating change in TL as a binary variable (attrition; yes, no) or as a continuous variable but with any observed increases recoded as 0, no significant associations between PA and PF from 1989–1993 and subsequent 5-yr change in TL were observed (Tables, Supplemental Digital Content 2 and 3, Additional statistical analyses, http://links.lww.com/MSS/A554, http://links.lww.com/MSS/A555). Longitudinal analysis of changes in PA and PA and change in TL. When we evaluated changes in PA and PF and changes in TL, change in leisure-time activity was associated with a trend toward less shortening in TL (P trend = 0.07), and change in chair test performance was associated with less shortening in TL (P trend = 0.04) (Table 4). For example, each 1000 kcalIwkj1 of increased leisure-time activity was associated with a trend toward less attrition (2.2 bpIyrj1; 95% CI, j0.18 to 4.6), and each 1-s change in the time needed to complete five chair stands was associated with 0.9 bpIyrj1 less attrition in TL (95% CI, 0.04 to 1.8). Other PA measures (such as walking pace, walking distance, and walking score) and other PF measures (such as walk test, hand grip test, and overall PF score) were not significantly associated with change in TL. When we excluded participants with poor self-reported health status or participants having any limitations in activities of daily living or instrumental activities, the associations of changes in leisure-time activity and chair test performance with change in TL were strengthened in magnitude (2.8 and 1.2 bpIyrj1, respectively) and statistical significance (P trend = 0.04 and 0.02, respectively). Results for sensitivity analyses recoding any observed increases in TL to no change were generally similar (Table, Supplemental Digital Content 4, Additional statistical analyses, http://links.lww.com/MSS/A556). Results of several sensitivity analyses were not appreciably altered, including further adjustment of both PA and PF measures to assess their independent associations with TL or further adjustment of baseline characteristics that could be either confounders or mediators of these relationships (see ‘‘Methods’’). In addition, we performed cumulative averaging, with 50% weight given to the most recent PA/PF measure; results were similar to those for equal weight cumulative averaging (data not shown).

DISCUSSION In this large prospective study of older adults (mean age at first measurement of TL, 73 yr), cross-sectional analyses suggested that greater walking distance and chair test performance are associated with longer TL. Furthermore, prospective analyses have shown that changes in leisure-time activity and chair test performance are associated with differences in changes in TL. The lack of prospective associations of other PA and PF metrics could be attributed to measurement error in TL due to DNA degradation, which would have diminished the ability to detect changes. Even so, even later in life, changes in certain PA and PF are associated



TABLE 2. Multivariable-adjusted cross-sectional associations of cumulatively averaged PA and PF between 1989–1990 and 1992–1993 and between 1993–1994 and 1997–1998 with TL from 1992 to 1993 and from 1997 to 1998 among 1164 older US adults. TL (95% CI), bpa All Participants (N = 582)

Excluding Participants with Limitations in Activities of Daily Living (n = 438)

Reference 10.2 (j21.6 to 42.0) j14.1 (j66.6 to 38.5) 0.86


Reference 54.7 (8.2 to 101.2) 62.6 (12.6 to 112.4) 66.6 (13.9 to 119.3) 109.6 (50.7 to 168.6) 0.002 Reference 23.2 (j32.3 to 22.1 (j44.9 to 38.6 (j19.9 to 37.1 (j21.8 to 0.95

78.6) 89.0) 97.1) 96.1)

Reference 17.3 (j32.6 to 67.2) 17.8 (j35.6 to 71.2) 24.9 (j30.4 to 80.1) 60.0 (j0.4 to 120.4) 0.06 Reference 28.9 (j28.1 to 17.8 (j51.4 to 28.0 (j30.9 to 30.2 (j28.8 to 0.49

85.9) 87.0) 86.9) 89.1)

Reference 6.0 (j47.6 to 59.6) 10.6 (j44.4 to 65.6) 35.9 (j34.7 to 106.4) 0.33

Reference 24.1 (j32.2 to 80.3) 35.6 (j23.5 to 94.7) 79.0 (4.2 to 153.9) 0.04

Reference 31.9 (j14.3 to 27.4 (j24.4 to 34.6 (j19.3 to 35.6 (j20.5 to 0.39

Reference 59.2 (9.9 to 108.5) 47.6 (j6.7 to 101.8) 44.2 (j13.7 to 102.1) 61.8 (2.4 to 121.2) 0.31

78.0) 79.2) 88.6) 91.8)

Reference 10.5 (j37.9 to 58.8) 41.1 (j11.9 to 94.1) 51.7 (j4.4 to 107.7) 33.2 (j29.3 to 95.6) 0.18


Reference j24.0 (j69.1 to j27.3 (j85.8 to j3.5 (j76.7 to 12.2 (j85.7 to 0.95

21.1) 31.3) 69.6) 110.1)

Reference j5.8 (j53.4 to 21.9 (j41.4 to 42.2 (j35.8 to 35.9 (j66.7 to 0.36

41.7) 85.4) 120.2) 138.5)


27.7) 57.3) 84.9) 94.1)


24.3) 49.8) 68.6) 73.4)

Reference j31.7 (j78.2 to j18.5 (j72.6 to 16.6 (j42.3 to 9.8 (j60.3 to 0.18

14.8) 35.7) 75.5) 80.0)

Reference j22.9 (j70.0 to 24.2) 1.0 (j52.7 to 54.8) 20.0 (j38.6 to 78.6) 13.5 (j54.42 to 80.7) 0.29

EPIDEMIOLOGY

PAb Walking pace G2 mph Reference 2–3 mph 9.5 (j18.3 to 37.4) 93 mph j19.5 (j67.3 to 28.3) P trend 0.78 Walking distance e5 blocks per week Reference 6–13 blocks per week 33.0 (j6.6 to 72.6) 14–27 blocks per week 36.9 (j6.9 to 80.8) 28–54 blocks per week 46.2 (j1.4 to 91.8) Q55 blocks per week 79.4 (27.6 to 131.3) P trend 0.007 Walking scorec I Reference II 17.9 (j26.9 to 62.8) III 4.3 (j53.1 to 61.7) IV 13.3 (j34.7 to 61.4) V 18.7 (j29.4 to 66.8) P trend 0.54 Intensity None Reference Low 28.4 (j14.8 to 71.6) Moderate 32.9 (j12.7 to 78.6) High 58.4 (j4.1 to 120.9) P trend 0.12 Leisure-time activity j1 Reference e104 kcalIwk 34.9 (j4.5 to 74.5) 105–420 kcalIwkj1 j1 28.9 (j15.5 to 73.4) 431–875 kcalIwk 889–1740 kcalIwkj1 35.3 (j11.4 to 82.1) 38.8 (j11.1 to 88.7) Q1761 kcalIwkj1 P trend 0.21 PFb Walk test (for every 15 ft)d Q6.7 s Reference 5.7–6.5 s 16.2 (j11.9 to 8.7) 5.0–5.5 s 37.5 (j10.3 to 8.5) 4.3–4.7 s 46.1 (j6.9 to 14.2) 3.0–4.0 s 31.5 (j15.1 to 7.5) P trend 0.20 Hand grip testd e19.6 kg Reference 19.7–23.6 kg j13.9 (j56.4 to 28.6) 23.7–28.8 kg j1.6 (j56.4 to 53.3) 29.1–37.1 kg 20.1 (j47.5 to 87.6) Q37.2 kg 37.9 (j52.9 to 128.7) P trend 0.47 Chair test (for every five chair stands)d Q17.0 s Reference 14.0–16.7 s j2.9 (j41.2 to 35.3) 12.3–13.7 s 8.5 (j34.5 to 51.5) 10.7–12.0 s 29.7 (j15.2 to 74.6) e10.6 s 39.8 (j7.3 to 86.9) P trend 0.04 c,d PF score I Reference II j11.6 (j50.7 to 27.4) III 8.8 (j35.9 to 53.6) IV 35.1 (j14.9 to 85.1) V 31.9 (j28.5 to 92.3) P trend 0.09

Excluding Participants with Poor or Fair Self-reported Health Status (n = 458)

a All analyses were adjusted for age (in years), sex (male, female), race (White, non-White), clinical site (four categories), education (less than high school, high school, more than high school), annual income (eUS$25,000, 9US$25,000), and smoking status (never smoker, former smoker, current smoker). b Cross-sectional (mixed-model) analysis according to cumulatively averaged PA and PF between 1997–1998 and 1992–1993. c Walking score is an ordinal score based on the combination of walking pace and walking distance. PF score is an ordinal score based on the combination of walk test, hand grip test, and chair test performance (each in quintiles). d Sample size included È25–40 fewer participants in each analysis due to missing data on exposure.




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TABLE 3. Multivariable-adjusted longitudinal associations of cumulatively averaged PA and PF between 1989 and 1993 with changes in TL between 1992–1993 and 1997–1998 among 582 older US adults. TL (95% CI), bpIyrj1a

EPIDEMIOLOGY

All Participants (N = 582)


PAb Walking pace G2 mph Reference 2–3 mph 1.2 (j6.2 to 8.4) 93 mph j2.8 (j12.5 to 6.8) P trend 0.62 Walking distance e5 blocks per week Reference 6–13 blocks per week j2.5 (j12.3 to 7.2) 14–27 blocks per week 7.4 (j2.2 to 16.9) 28–54 blocks per week j1.4 (j10.9 to 8.2) Q55 blocks per week 3.3 (j6.4 to 13.0) P trend 0.50 Walking scorec I Reference II 0.6 (j12.0 13.3) III 2.7 (j9.2 to 14.5) IV 4.0 (j6.9 to 14.9) V 3.5 (j8.2 to 15.2) P trend 0.43 Intensity None Reference Low j8.7 (j24.3 to 6.8) Moderate j9.4 (j24.9 to 6.2) High j0.3 (j17.9 to 17.3) P trend 0.59 Leisure-time activity j1 Reference e104 kcalIwk j2.3 (j11.6 to 7.1) 105–420 kcalIwkj1 j1 4.2 (j5.4 to 13.7) 431–875 kcalIwk 889–1740 kcalIwkj1 4.3 (j5.4 to 14.0) j1.9 (j11.8 to 7.9) Q1761 kcalIwkj1 P trend 0.83 PFb Walk test (for every 15 ft)d Q6.7 s Reference 5.7–6.5 s j1.6 (j11.9 to 8.7) 5.0–5.5 s j0.9 (j10.3 to 8.5) 4.3–4.7 s 3.6 (j6.9 to 14.2) 3.0–4.0 s 3.8 (j15.1 to 7.5) P trend 0.94 Hand grip testd e19.6 kg Reference 19.7–23.6 kg 5.0 (j4.4 to 14.4) 23.7–28.8 kg 10.7 (1.1 to 20.3) 29.1–37.1 kg 8.6 (j2.9 to 20.2) Q37.3 kg 9.7 (j4.4 to 23.7) P trend 0.07 Chair test (for every five chair stands)d Q17.0 s Reference 14.0–16.7 s j1.4 (j10.9 to 8.1) 12.3–13.7 s j2.9 (j12.5 to 6.6) 10.7–12.0 s 2.7 (j6.6 to 12.1) e10.6 s j3.2 (j13.4 to 6.9) P trend 0.93 PF scorec,d I Reference II 3.5 (j6.4 to 13.5) III 4.1 (j5.8 to 13.9) IV 2.2 (j8.3 to 12.7) V j0.2 (j13.1 to 12.8) P trend 0.94



Reference 6.5 (j3.0 to 16.0) 2.9 (j8.6 to 14.3) 0.74

Reference (j15.3 to 7.6) (j5.1 to 17.1) (j12.9 to 8.3) (j7.9 to 14.3) 0.56

Reference j0.8 (j13.2 to 11.5) 12.7 (0.9 to 24.4) j0.3 (j11.8 to 11.2) 3.9 (j7.6 to 15.3) 0.69



Reference j9.5 (j28.6 to 9.6) j10.4 (j29.4 to 8.6) 0.8 (j20.1 to 21.6) 0.44

Reference j11.5 (j34.1 to 11.2) j14.3 (j36.9 to 8.3) j3.4 (j27.7 to 20.9) 0.76

j3.9 6.0 j2.8 3.1

1.5 6.2 5.4 6.2

0.6 7.5 5.7 0.5


j1.2 3.2 5.9 j2.2

Reference (j13.0 to 10.6) (j8.6 to 15.1) (j6.2 to 18.0) (14.1 to 9.7) 0.98

1.6 0.3 4.5 j1.7


2.4 4.1 9.6 0.7


1.7 6.3 4.7 3.8


2.9 j3.9 4.1 0.7


1.6 j2.0 4.2 j2.5

1.9 0.2 2.9 j1.6


4.7 4.7 4.1 3.4

Reference 7.8 (j3.3 to 18.9) 12.8 (0.9 to 24.7) 6.7 (j7.4 to 20.8) 11.9 (j5.3 to 29.1) 0.13 Reference (j8.9 to 12.1) (j12.7 to 8.6) (j6.4 to 14.7) (j14.4 to 9.4) 0.98 Reference (j8.7 to 18.0) (j8.6 to 17.9) (j9.7 to 17.9) (j12.6 to 19.3) 0.84

a Rate of change in TL (bpIyrj1 ) = (TL1997–1998 j TL1992–1993)/follow-up years. Positive values indicate lesser shortening in TL based on comparison to the reference group, whereas negative values indicate greater shortening in TL. All analyses were adjusted for age (in years), sex (male, female), race (White, non-White), clinical site (four categories), education (less than high school, high school, more than high school), annual income (eUS$25,000, 9US$25,000), and smoking status (never smoker, former smoker, current smoker). b Longitudinal analysis according to cumulatively averaged PA and PF for 1989–1990, 1990–1991, 1991–1992, 1992–1993 (or those available). c Walking score is an ordinal score based on the combination of walking pace and walking distance. PF score is an ordinal score based on the combination of walk test, hand grip test, and chair test performance (each in quintiles). d Sample size included È25–40 fewer participants in each analysis due to missing data on exposure.

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TABLE 4. Multivariable-adjusted associations of changes in cumulatively averaged PA and PF between 1989–1993 and 1993–1998 with changes in TL between 1992–1993 and 1997–1998 among 582 older US adults. Difference in TL (95% CI), bpIyrj1a All Participants (N = 582) PAb Change in walking pace (mph) for every one-unit increase j0.6 (j4.9 to 3.7) (ej2: 3.3%;j1.5 to j1: 24.3%; 0.5: 15.0%; 0: 44.6%; 0.5: 6.9%; Q1: 5.9%)c P 0.78 Change in walking distance (blocks per week) for every one-unit increase Mean T SD, j7.7 T 33.5 0.04 (j0.05 to 0.13) 10th–90th percentile, j42.1 to 22.1 P 0.40 Change in walking score for every one-unit increased (ej1.3: 4.1%; j1: 20%; j0.75 to j0.25: 5.7%; j1.6 (j5.5 to 2.2) 0: 50.8%; 0.27 to 0.74: 3.4%; Q2: 16%)c P 0.41 Change in leisure-time activity (kcalIwkj1) for every one-unit increase Mean T SD, j345.9 T 1238.8 2.2 (j0.18 to 4.6) 10th–90th percentile, j1653.8 to 735 P 0.07 PFb e Change in walk test (s; for every 15 ft) for every one-unit increase Mean T SD, 0.4 T 1.9 0.2 (j1.4 to 1.8) 10th–90th percentile, j0.9 to 1.8 P 0.80 Change in hand grip test (kg) for every one-unit increasee Mean T SD, j0.6 T 3.6 0.4 (j0.5 to 1.3) 10th–90th percentile, j5.0 to 3.7 P 0.37 Change in chair test (s; for every five chair stands) for every one-unit increasee Mean T SD, 2.2 T 3.6 0.9 (0.04 to 1.8) 10th–90th percentile, j1.7 to 6.5 P 0.04 Change in PF score for every one-unit increased,e (ej1: 18.7%; 0: 43.9%; Q1: 37.4%)c j2.2 (j6.0 to 1.6) P 0.25


j2.0 (j6.9 to 2.8) 0.41


j3.2 (j8.2 to 1.7) 0.20

j0.01 (j0.10 to 0.1) 0.90

0.06 (j0.03 to 0.15) 0.19

j3.1 (j7.5 to 1.3) 0.17

j0.7 (j5.1 to 3.8) 0.76

2.3 (j0.20 to 4.8) 0.07

2.8 (0.15 to 5.4) 0.04

0.5 (j1.2 to 2.3) 0.56

2.1 (j0.5 to 4.6) 0.11

0.4 (j0.6 to 1.4) 0.41

0.3 (j0.7 to 1.3) 0.60

1.1 (0.5 to 2.2) 0.04

1.2 (0.2 to 2.2) 0.02

j2.7 (j6.8 to 1.3) 0.19

j2.4 (j6.6 to 1.7) 0.25

Rate of change in TL (bpIyrj1 ) = (TL1997–1998 j TL1992–1993)/follow-up years. Positive values indicate lesser shortening in TL, whereas negative values indicate greater shortening in TL. All analyses were adjusted for age (in years), sex (male, female), race (White, non-White), clinical site (four categories), education (less than high school, high school, more than high school), annual income (eUS$25,000, 9US$25,000), and smoking status (never smoker, former smoker, current smoker). b Longitudinal analysis according to differences in cumulatively averaged PA and PF between 1997–1998 and 1992–1993. c Categories of change and proportion of participants in each category. d Walking score is an ordinal score based on the combination of walking pace and walking distance. PF score is an ordinal score based on the combination of walk test, hand grip test, and chair test performance (each in quintiles). e Sample size included È25–40 fewer participants in each analysis due to missing data on exposure. a


plausibility of our findings is supported by the putative pathways of telomere loss, which are thought to be related to cumulative burdens of oxidative stress and inflammation (2,14), and by the pathways of benefits of regular PA, which include upregulation of antioxidant defense systems (15) and reduced chronic systemic inflammation (41). By these and other pathways, PA may reduce oxidative DNA damage (33,39); for example, duration of exercise has been inversely correlated with biomarkers for DNA and telomere damage and with p16 expression, a biomarker for cellular aging (39). Interestingly, a bout of acute exercise increases production of free radicals, depending on intensity and duration (15). This pro-oxidant response may be necessary for the activation of beneficial antioxidant and other cellular defense systems (29), through which habitual long-term PA (such as that evaluated in this study) may lead to beneficial physiological adaptations (15). Another possible explanatory pathway might be through upregulation of telomerase reverse transcriptase, which seems to occur after exercise (14). For example, mechanisms for the beneficial effects of omega-3 fatty acids and



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with TL, suggesting that greater leisure-time activity and fitness could reduce leukocyte attrition among older adults. Telomeres are caplike nucleoproteins at chromosome ends that protect genome from degradation and interchromosomal fusion (16,35). In the normal cellular process, a small portion of telomeric DNA is lost with each cell division; when a limit length is achieved, a cell undergoes apoptosis (35). Normally with aging, chromosomes become increasingly impaired due to DNA damage, eventually leading to apoptotic signals and cell death; however, telomeres can prevent or delay such damage (16). It has been hypothesized that certain lifestyles factors may accelerate telomere shortening and consequently affect health, healthy aging, and longevity (35). Shorter TL is associated with several age-related diseases (39), including CVD and type 2 diabetes (11). Our findings of longer telomeres with some measures of greater PA and PF at baseline and less telomere attrition with some measures of changes in PA and PF longitudinally suggest that PA and PF could influence pathways related to TL. Such an effect could, for example, partly account for the beneficial associations of PA and PF with many age-related diseases (35,39). The biologic

EPIDEMIOLOGY

PA on survival after acute myocardial infarction could be related to elevation in telomerase expression, resulting in higher regeneration potential (31,45). Although controversial, some evidence suggests that leucocyte TL could actually elongate across a decade (24); however, others believe that apparent elongation is mainly due to measurement error (40). There has been no consensus on this potential for telomere lengthening; further studies on this topic are needed. In the present work, we observed similarities and differences in cross-sectional versus prospective analyses. In cross-sectional analyses, walking distance, but not leisuretime activity, was associated with longer TL. Conversely, in prospective analyses, leisure-time activity, but not walking distance, was associated with differences in changes in TL. Interestingly, chair test performance was associated with both cross-sectional and prospective analyses. The reasons for these specific associations are unknown, and our novel findings highlight the need to further investigate how different types of PA and different measures of PF may influence TL. The American College of Sports Medicine and the American Heart Association recommend that older adults engage in at least 30 min of moderate PA on most days of the week (1). Our results support these general guidelines by suggesting that long-term PA may influence telomere dynamics later in life. Previous studies of PA and TL have provided inconsistent results; only four studies were conducted in older adults (18,28,34,47). Among these, one cross-sectional study of 2006 older Chinese participants reported no association between PA and TL (47); the other three studies, which were also cross-sectional but were conducted in much smaller samples (N = 32–204), found positive associations between PA and TL (18,28,34). Our results are consistent with these latter three cross-sectional studies and with other crosssectional studies of middle-age and younger participants linking higher PA to longer TL (7,8,17,18,21,28,34,48). Our findings build upon and expand these previous results by evaluating both cross-sectional and longitudinal associations of PA, PF, and TL (including changes in both) in a wellestablished cohort of older US adults. Our analysis had several strengths. Information on PA, PF, TL, and other risk factors was prospectively assessed using standardized methods. Participants were randomly selected and enrolled from Medicare eligibility lists in several US communities, providing a community-based sample of older adults. Serial measures of PA allowed for evaluation of cumulatively updated PA, reducing misclassification and providing a better measure of longer-term PA. Serial measures also allowed for a novel evaluation of how changes in PA relate to changes in TL. Prospective analyses and sensitivity analyses excluding less healthy participants reduced the potential for reverse causation, and adjustment for a wide range of covariates minimized the potential impact of confounding.

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Potential limitations were also present. Measurement error in TL (in particular, change in TL) would diminish the ability to detect associations, which would cause underestimation of the magnitude and statistical significance of our findings. In addition, the TL quantification technique used is a less sensitive method for identifying subtle differences between individuals and requires high-quality DNA. We evaluated several different PA and PF indices, increasing the possibility of chance findings. However, several of our findings are consistent with those of other studies, and one could consider each PA or PF and TL association as a separate hypothesis. Borderline P values should be interpreted with caution, with careful attention to both internal consistency and biologic plausibility. PA measures were obtained from self-reports and may appropriately reflect relative ordering (ranking) of participants but not precise quantitative levels of energy expenditure. Although a range of covariates were available and evaluated as potential confounders and although findings from sensitivity analyses were similar, residual confounding due to unknown or incompletely measured factors cannot be excluded. The assessments of PA, PF, and TL were subject to random error and biologic variability, which would attenuate findings toward the null. The prospective associations of cumulatively updated PA with TL could also partly reflect the effects of PA earlier in life; in contrast, the associations of changes in PA with TL would not be confounded by PA at younger ages. Different participants had different numbers of exposure measures and, thus, possibly different precisions of exposure. Results were attained from older, predominantly White Americans and may not be directly generalizable to other populations. Furthermore, our results may only be generalized to leukocyte TL because it may not reflect TL dynamics in other tissues. Conversely, leukocyte TL is the most commonly measured TL metric and has been associated with diverse exposures and disease end points in prior studies. In sum, our results suggest that greater walking distance and chair test performance are cross-sectionally associated with longer TL and that changes in leisure-time activity and chair test performance are associated with differences in changes in TL. These results suggest that PA and PF may play a role in the regulation of TL during the aging process.

The authors express their gratitude to the Cardiovascular Health Study participants. A full list of participating Cardiovascular Health Study investigators and institutions is available at http://www.chsnhlbi.org. This research was supported by contracts HHSN268201200036C, HHSN268200800007C, N01HC55222, N01HC85079, N01HC85080, N01HC85081, N01HC85082, N01HC85083, and N01HC85086, and by grant U01HL080295 from the National Heart, Lung, and Blood Institute, with additional contributions from the National Institute of Neurological Disorders and Stroke. Additional support was provided by grant R01AG023629 from the National Institute on Aging. Luisa Soares-Miranda is supported by the grant SFRH/BPD/ 76947/2011 funded by FCT (QREN - POPH - Type 4.1 - Advanced



training, subsidized by the European Social Fund and national funds of MEC), PTDC/DES/099018/2008 - FCT/FCOMP-01- 0124-FEDER009573, and The Research Centre in Physical Activity Health and Leisure is supported by FCT: UID/DTP/00617/2013. Fumiaki Imamura was supported by the Medical Research Council Epidemiology Unit (grant MC_UU_12015/5).

The funders had no role in the design or conduct of the study; in the collection, management, analysis, or interpretation of data; or in the preparation, review, or approval of the manuscript. The results of the present study do not constitute endorsement by the American College of Sports Medicine. The authors declare no conflicts of interest.

REFERENCES


19. Longstreth WT Jr, Bernick C, Fitzpatrick A, et al. Frequency and predictors of stroke death in 5,888 participants in the Cardiovascular Health Study. Neurology. 2001;56(3):368–75. 20. Lopez-Otin C, Blasco MA, Partridge L, Serrano M, Kroemer G. The hallmarks of aging. Cell. 2013;153(6):1194–217. 21. Ludlow AT, Zimmerman JB, Witkowski S, Hearn JW, Hatfield BD, Roth SM. Relationship between physical activity level, telomere length, and telomerase activity. Med Sci Sports Exerc. 2008;40(10):1764–71. 22. McArdle A, Jackson MJ. Exercise, oxidative stress and ageing. J Anat. 2000;197(Pt 4):539–41. 23. Mozaffarian D, Furberg CD, Psaty BM, Siscovick D. Physical activity and incidence of atrial fibrillation in older adults: the Cardiovascular Health Study. Circulation. 2008;118(8):800–7. 24. Nordfjall K, Svenson U, Norrback KF, Adolfsson R, Lenner P, Roos G. The individual blood cell telomere attrition rate is telomere length dependent. PLoS Genet. 2009;5(2):e1000375. 25. Okuda K, Khan MY, Skurnick J, Kimura M, Aviv H, Aviv A. Telomere attrition of the human abdominal aorta: relationships with age and atherosclerosis. Atherosclerosis. 2000;152(2): 391–8. 26. Ornish D, Lin J, Daubenmier J, et al. Increased telomerase activity and comprehensive lifestyle changes: a pilot study. Lancet Oncol. 2008;9(11):1048–57. 27. Psaty BM, Kuller LH, Bild D, et al. Methods of assessing prevalent cardiovascular disease in the Cardiovascular Health Study. Ann Epidemiol. 1995;5(4):270–7. 28. Puterman E, Lin J, Blackburn E, O_Donovan A, Adler N, Epel E. The power of exercise: buffering the effect of chronic stress on telomere length. PLoS One. 2010;5(5):e10837. 29. Ristow M, Zarse K, Oberbach A, et al. Antioxidants prevent health-promoting effects of physical exercise in humans. Proc Natl Acad Sci U S A. 2009;106(21):8665–70. 30. Sampson MJ, Winterbone MS, Hughes JC, Dozio N, Hughes DA. Monocyte telomere shortening and oxidative DNA damage in type 2 diabetes. Diabetes Care. 2006;29(2):283–9. 31. Sanchis-Gomar F, Lucia A. Acute myocardial infarction: Ftelomerasing_ for cardioprotection. Trends Mol Med. 2015;21(4):203–5. 32. Sanders JL, Fitzpatrick AL, Boudreau RM, et al. Leukocyte telomere length is associated with noninvasively measured age-related disease: the Cardiovascular Health Study. J Gerontol A Biol Sci Med Sci. 2012;67(4):409–16. 33. Sattelmair JR, Pertman JH, Forman DE. Effects of physical activity on cardiovascular and noncardiovascular outcomes in older adults. Clin Geriatr Med. 2009;25(4):677–702, viii–ix. 34. Savela S, Saijonmaa O, Strandberg TE, et al. Physical activity in midlife and telomere length measured in old age. Exp Gerontol. 2013;48(1):81–4. 35. Shammas MA. Telomeres, lifestyle, cancer, and aging. Curr Opin Clin Nutr Metab Care. 2011;14(1):28–34. 36. Shin YA, Lee JH, Song W, Jun TW. Exercise training improves the antioxidant enzyme activity with no changes of telomere length. Mech Ageing Dev. 2008;129(5):254–60. 37. Siscovick DS, Fried L, Mittelmark M, et al. Exercise intensity and subclinical cardiovascular disease in the elderly: the Cardiovascular Health Study. Am J Epidemiol. 1997;145(11):977–86. 38. Sjogren P, Fisher R, Kallings L, Svenson U, Roos G, Hellenius ML. Stand up for health—avoiding sedentary behaviour might



2533

EPIDEMIOLOGY

1. American College of Sports Medicine, Chodzko-Zajko WJ, Proctor DN, et al. American College of Sports Medicine Position Stand: exercise and physical activity for older adults. Med Sci Sports Exerc. 2009;41(7):1510–30. 2. Aviv A. Telomeres and human aging: facts and fibs. Sci Aging Knowledge Environ. 2004;(51):pe43. 3. Benetos A, Gautier S, Lafleche A, et al. Blockade of angiotensin II type 1 receptors: effect on carotid and radial artery structure and function in hypertensive humans. J Vasc Res. 2000;37(1):8–15; discussion 68–70. 4. Benetos A, Okuda K, Lajemi M, et al. Telomere length as an indicator of biological aging: the gender effect and relation with pulse pressure and pulse wave velocity. Hypertension. 2001; 37(2):381–5. 5. Burnett-Hartman AN, Fitzpatrick AL, Kronmal RA, et al. Telomereassociated polymorphisms correlate with cardiovascular disease mortality in Caucasian women: the Cardiovascular Health Study. Mech Ageing Dev. 2012;133(5):275–81. 6. Cassidy A, De Vivo I, Liu Y, et al. Associations between diet, lifestyle factors, and telomere length in women. Am J Clin Nutr. 2010;91(5):1273–80. 7. Cherkas LF, Hunkin JL, Kato BS, et al. The association between physical activity in leisure time and leukocyte telomere length. Arch Intern Med. 2008;168(2):154–8. 8. Du M, Prescott J, Kraft P, et al. Physical activity, sedentary behavior, and leukocyte telomere length in women. Am J Epidemiol. 2012;175(5):414–22. 9. Epel ES, Merkin SS, Cawthon R, et al. The rate of leukocyte telomere shortening predicts mortality from cardiovascular disease in elderly men. Aging. 2009;1(1):81–8. 10. Farzaneh-Far R, Lin J, Epel ES, Harris WS, Blackburn EH, Whooley MA. Association of marine omega-3 fatty acid levels with telomeric aging in patients with coronary heart disease. JAMA. 2010;303(3):250–7. 11. Fitzpatrick AL, Kronmal RA, Gardner JP, et al. Leukocyte telomere length and cardiovascular disease in the Cardiovascular Health Study. Am J Epidemiol. 2007;165(1):14–21. 12. Fitzpatrick AL, Kronmal RA, Kimura M, et al. Leukocyte telomere length and mortality in the Cardiovascular Health Study. J Gerontol A Biol Sci Med Sci. 2011;66(4):421–9. 13. Fried LP, Borhani NO, Enright P, et al. The Cardiovascular Health Study: design and rationale. Ann Epidemiol. 1991;1(3): 263–76. 14. Garatachea N, Pareja-Galeano H, Sanchis-Gomar F, et al. Exercise attenuates the major hallmarks of aging. Rejuvenation Res. 2015; 18(1):57–89. 15. Gomes EC, Silva AN, de Oliveira MR. Oxidants, antioxidants, and the beneficial roles of exercise-induced production of reactive species. Oxid Med Cell Longev. 2012;2012:756132. 16. Kelly DP. Cell biology: ageing theories unified. Nature. 2011; 470(7334):342–3. 17. Kim JH, Ko JH, Lee DC, Lim I, Bang H. Habitual physical exercise has beneficial effects on telomere length in postmenopausal women. Menopause. 2012;19(10):1109–15. 18. LaRocca TJ, Seals DR, Pierce GL. Leukocyte telomere length is preserved with aging in endurance exercise-trained adults and related to maximal aerobic capacity. Mech Ageing Dev. 2010; 131(2):165–7.

39.

40.

41.

42.

44. Tell GS, Fried LP, Hermanson B, Manolio TA, Newman AB, Borhani NO. Recruitment of adults 65 years and older as participants in the Cardiovascular Health Study. Ann Epidemiol. 1993; 3(4):358–66. 45. Terai M, Izumiyama-Shimomura N, Aida J, et al. Association of telomere shortening in myocardium with heart weight gain and cause of death. Sci Rep. 2013;3:2401. 46. Varela E, Blasco MA. 2009 Nobel prize in physiology or medicine: telomeres and telomerase. Oncogene. 2010;29(11):1561–5. 47. Woo J, Tang N, Leung J. No association between physical activity and telomere length in an elderly Chinese population 65 years and older. Arch Intern Med. 2008;168(19):2163–4. 48. Zhu H, Wang X, Gutin B, et al. Leukocyte telomere length in healthy Caucasian and African-American adolescents: relationships with race, sex, adiposity, adipokines, and physical activity. J Pediatr. 2011;158(2):215–20.

EPIDEMIOLOGY

43.

lengthen your telomeres: secondary outcomes from a physical activity RCT in older people. Br J Sports Med. 2014;48(19):1407–9. Song Z, von Figura G, Liu Y, et al. Lifestyle impacts on the agingassociated expression of biomarkers of DNA damage and telomere dysfunction in human blood. Aging Cell. 2010;9(4):607–15. Steenstrup T, Hjelmborg JV, Kark JD, Christensen K, Aviv A. The telomere lengthening conundrum—artifact or biology? Nucleic Acids Res. 2013;41(13):e131. Stewart LK, Flynn MG, Campbell WW, et al. The influence of exercise training on inflammatory cytokines and C-reactive protein. Med Sci Sports Exerc. 2007;39(10):1714–9. Svenson U, Nordfjall K, Baird D, et al. Blood cell telomere length is a dynamic feature. PLoS One. 2011;6(6):e21485. Taylor HL, Jacobs DR, Schucker B, Knudsen J, Leon AS, Debacker G. A questionnaire for the assessment of leisure time physical activities. J Chronic Dis. 1978;31(12):741–55.

2534




Leukocyte telomere length and ideal cardiovascular health in American Indians: the Strong Heart Family Study.

Physical activity and cardiovascular health.

Physical activity, cardiovascular fitness, and adiposity in children.

Participation in physical activity and cardiovascular fitness have different effects on mental health and mood.

NAVIGATOR: physical activity for cardiovascular health?

Physical activity, fitness, and vascular health in patients with asthma.

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Constructing cardiovascular fitness knowledge in physical education.

Physical activity in overweight and obese adolescents: systematic review of the effects on physical fitness components and cardiovascular risk factors.

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Physical activity and telomere length: Impact of aging and potential mechanisms of action.

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Association between Snoring and Leukocyte Telomere Length.

Effects of physical activity in telomere length: Systematic review and meta-analysis.

Physical activity and telomere length in early stage breast cancer survivors.

Coffee Consumption Is Positively Associated with Longer Leukocyte Telomere Length in the Nurses' Health Study.

Leisure time physical activity and long-term cardiovascular and cancer outcomes: the Busselton Health Study.

Associations of leisure time, commuting, and occupational physical activity with physical fitness and cardiovascular risk factors in young men.

Serum vaspin levels are associated with physical activity or physical fitness in Japanese: a pilot study.