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The American Journal of Clinical Nutrition logoLink to The American Journal of Clinical Nutrition
. 2018 Sep 11;108(3):453–475. doi: 10.1093/ajcn/nqy107

Body mass index is negatively associated with telomere length: a collaborative cross-sectional meta-analysis of 87 observational studies

Marij Gielen 1,✉,3, Geja J Hageman 2, Evangelia E Antoniou 3, Katarina Nordfjall 4, Massimo Mangino 5,6, Muthuswamy Balasubramanyam 7, Tim de Meyer 8, Audrey E Hendricks 9,10, Erik J Giltay 11, Steven C Hunt 12, Jennifer A Nettleton 13, Klelia D Salpea 14, Vanessa A Diaz 15, Ramin Farzaneh-Far 16, Gil Atzmon 17, Sarah E Harris 18, Lifang Hou 19, David Gilley 20, Iiris Hovatta 21,22, Jeremy D Kark 23, Hisham Nassar 24, David J Kurz 25, Karen A Mather 26, Peter Willeit 27, Yun-Ling Zheng 28, Sofia Pavanello 29, Ellen W Demerath 30, Line Rode 31, Daniel Bunout 32, Andrew Steptoe 33, Lisa Boardman 34, Amelia Marti 35,36,37, Belinda Needham 38, Wei Zheng 39, Rosalind Ramsey-Goldman 40, Andrew J Pellatt 41,2, Jaakko Kaprio 42,43, Jonathan N Hofmann 44, Christian Gieger 45, Giuseppe Paolisso 46, Jacob B H Hjelmborg 47, Lisa Mirabello 46, Teresa Seeman 48, Jason Wong 49, Pim van der Harst 50, Linda Broer 51, Florian Kronenberg 52, Barbara Kollerits 52, Timo Strandberg 53, Dan T A Eisenberg 54, Catherine Duggan 55, Josine E Verhoeven 56, Roxanne Schaakxs 56, Raffaela Zannolli 57, Rosana M R dos Reis 58, Fadi J Charchar 59, Maciej Tomaszewski 60,61, Ute Mons 62,63, Ilja Demuth 64, Andrea Elena Iglesias Molli 65, Guo Cheng 66, Dmytro Krasnienkov 67, Bianca D'Antono 68, Marek Kasielski 69, Barry J McDonnell 70, Richard Paul Ebstein 71, Kristina Sundquist 72, Guillaume Pare 73, Michael Chong 73, Maurice P Zeegers 1,74; TELOMAAS group
PMCID: PMC6454526  PMID: 30535086

ABSTRACT

Background

Even before the onset of age-related diseases, obesity might be a contributing factor to the cumulative burden of oxidative stress and chronic inflammation throughout the life course. Obesity may therefore contribute to accelerated shortening of telomeres. Consequently, obese persons are more likely to have shorter telomeres, but the association between body mass index (BMI) and leukocyte telomere length (TL) might differ across the life span and between ethnicities and sexes.

Objective

A collaborative cross-sectional meta-analysis of observational studies was conducted to investigate the associations between BMI and TL across the life span.

Design

Eighty-seven distinct study samples were included in the meta-analysis capturing data from 146,114 individuals. Study-specific age- and sex-adjusted regression coefficients were combined by using a random-effects model in which absolute [base pairs (bp)] and relative telomere to single-copy gene ratio (T/S ratio) TLs were regressed against BMI. Stratified analysis was performed by 3 age categories (“young”: 18–60 y; “middle”: 61–75 y; and “old”: >75 y), sex, and ethnicity.

Results

Each unit increase in BMI corresponded to a −3.99 bp (95% CI: −5.17, −2.81 bp) difference in TL in the total pooled sample; among young adults, each unit increase in BMI corresponded to a −7.67 bp (95% CI: −10.03, −5.31 bp) difference. Each unit increase in BMI corresponded to a −1.58 × 10−3 unit T/S ratio (0.16% decrease; 95% CI: −2.14 × 10−3, −1.01 × 10−3) difference in age- and sex-adjusted relative TL in the total pooled sample; among young adults, each unit increase in BMI corresponded to a −2.58 × 10−3 unit T/S ratio (0.26% decrease; 95% CI: −3.92 × 10−3, −1.25 × 10−3). The associations were predominantly for the white pooled population. No sex differences were observed.

Conclusions

A higher BMI is associated with shorter telomeres, especially in younger individuals. The presently observed difference is not negligible. Meta-analyses of longitudinal studies evaluating change in body weight alongside change in TL are warranted.

Keywords: BMI, telomere length, obesity, low-grade inflammation, meta-analysis, observational studies

INTRODUCTION

Telomeres, the nucleoprotein structures at the ends of chromosomes, shorten with each cell division in somatic cells (1). When telomere length (TL) reaches a critical value, cells either enter a state of senescence or undergo apoptosis (2). Oxidative stress and chronic inflammation are suggested to play a role in accelerated telomere attrition (3–5). Even before the onset of age-related diseases, obesity might be a contributing factor to the cumulative burden of oxidative stress and chronic inflammation throughout the life course, and obesity may therefore contribute to accelerated shortening of telomeres.

Obesity is a growing health problem, and worldwide its prevalence has more than doubled since 1980 (6). In addition, the burden of diabetes and cardiovascular disease is partly attributable to being overweight and obese (6). Tackling obesity might be a starting point to delay telomere shortening and the onset of age-related diseases. Although obesity is associated with shorter telomeres overall (7), studies in the elderly found no relation between TL and obesity and no relation between TL and mortality (8, 9). We hypothesized that obese persons will have shorter telomeres, compared with those of normal weight of the same chronological age, but that the association between obesity and TL will differ across the life span.

Sex and ethnicity may also influence the association between BMI and TL. On average, women have longer telomeres than men (10–12). However, published results on sex differences in associations between BMI and TL are inconsistent (13–15). African Americans and Native Americans have higher rates of obesity (16), and racial differences in TL have frequently been reported in adult African Americans who have longer telomeres than white individuals (17–21), but evidence is lacking with regard to whether the association between BMI and TL differs between ethnicities.

Two recent meta-analyses reported the negative association between BMI and TL on reported summary statistics in the literature, but they did not examine sex differences or the influence of age and ethnicity (7, 22). To further evaluate whether BMI is associated with TL, a large-scale, collaborative, cross-sectional meta-analysis was conducted across observational studies that collected information on BMI and TL of adult individuals. To avoid publication bias and maximize the data in the analyses, a consistent standardized analysis plan across studies was used and principal investigators (PIs) of published studies were contacted and asked to participate in the Telomere Maastricht collaborator (TELOMAAS) group. Because the relation between TL and BMI could be moderated by age, sex, and ethnicity, we completed additional analyses stratifying by these factors.

METHODS

Search strategy

We performed a broad literature search up until 10 November 2017 using PubMed (www.ncbi.nlm.nih.gov/pmc), EMBASE (ovidsp.tx.ovid.com), and the Cochrane (www.cochranelibrary.com) database without restrictions in language or publication date. Numerous studies have measured BMI and TL for purposes other than the association between TL and BMI as an outcome. Therefore, the search was rather broad and not narrowed to TL or BMI. On the basis of the existing relation between obesity, diabetes, and cardiovascular diseases, and because TL is related to aging, we completed a search in which terms related to these conditions were entered. In addition, search items related to study design were entered. The complete search criteria are listed in Supplemental Methods. Citation and reference tracking were performed until no new studies were found. One of the authors (MG) performed the literature search and selected potentially relevant publications. Titles and abstracts of potentially relevant studies were screened. In addition, when the abstract indicated that the article reported a study of diabetes or cardiovascular disease, the full text was screened. No additional restrictions for study design were applied.

Eligibility criteria

Studies were included if height and weight or BMI was collected. The corresponding author was invited to participate in the meta-analysis and identified additional unpublished studies. PIs of these unpublished studies were also invited to participate. Cohort studies in healthy individuals at baseline were included, and if the study design was a case-control study, only controls were included in the meta-analysis. In compiling the database, care was taken to exclude overlapping study cohorts. The study sample (abbreviated as “study”) was taken as the unit for this meta-analysis.

Data extraction

The detailed study protocol can be found in the Supplemental data (“Study Protocol for Participating PIs”). The PI of each study completed a questionnaire and additional information was extracted from the manuscript. The following data were collected: study name; study design (cohort or case-control); sample size (cohort size or control group size); presence of the variables age, sex, and ethnicity [when ≥70% of the individuals of a sample were of a single ethnicity (e.g., white, African American, Native American, Asian, Hispanic), the sample was classified as a sample of a particular ethnicity; when no ethnicity constituted 70% of the sample, the sample was classified as a mixed sample]; leukocyte TL; and BMI (kg/m2) and whether BMI was measured or self-reported; white blood cell types from which DNA was extracted for telomere measurements; and method of TL measurement and of DNA storage (Supplemental Material: Study Protocol for Participating PIs). Two metrics were used for TL: absolute TL in base pairs (bp) and relative TL based on telomere to single-copy gene ratio (T/S ratio) (23). A T/S ratio of 0.8 indicates a relative TL, which is 80% of the reference used (100%).

The PI was free to provide the de-identified raw data or to perform linear regression analyses and provide summary statistics. If the PI provided raw data, one of the authors (MG) conducted the linear regression analyses with TL (bp or T/S ratio) as the outcome and BMI as the independent variable to obtain the summary statistics. Three sex groups were defined: men, women, and a combined group of men and women; 4 age groups were defined: “young” (18–60 y), “middle” (61–75 y), and “old” (>76 y) and a combined group of all subjects regardless of their age. For each study, ≤12 stratified linear regression analyses were conducted (stratified by the 3 sex groups and 4 age groups). The analyses that included all subjects regardless of sex were adjusted for sex; similarly, the analyses that included all subjects regardless of age were adjusted for age.

If the T/S ratio was used to estimate absolute TL without the use of reference DNA with the known absolute TL, the PI was asked to provide new analyses with the T/S ratio as the outcome. If the PI did not respond to this request, absolute TL based on the T/S ratio was used for analyses and included in the analysis. The regression coefficients (β estimates) and SEs were then used in the meta-analyses. In the case of longitudinal data, one randomly selected measurement of TL along with the corresponding BMI and age for that time point were used in the analysis. The summary statistics thus included the results of 12 linear regression analyses, with TL (bp or T/S ratio) as the outcome and BMI as the independent variable.

Assessment of small study effects

To examine the potential presence of publication bias, visual inspection of funnel plots for asymmetry was performed, followed by the Egger and Begg's linear regression test for small study effects (24) and the Duval and Tweedie nonparametric “trim and fill” method (25).

Statistical analysis

Statistical pooling

The primary outcome of the meta-analysis was a pooled estimation of the difference in absolute TL in bp or relative TL (T/S ratio) per unit increase in BMI. Study-specific regression coefficients (β estimates) and SEs were combined by using random-effects pooling in 12 meta-analyses. The assumption of a linear association between BMI and TL was verified by using the raw data provided by the PIs (Supplemental Methods, Supplemental Results).

Assessment of heterogeneity

Details are given in Supplemental Methods. Statistical heterogeneity between studies was estimated by using I2 statistics (26, 27) for each of the 12 meta-analyses. Low heterogeneity was indicated by I2 ≤25%, medium heterogeneity by I2 of 25–50%, and high heterogeneity by I2 >50% (27). To investigate potential effect modification of age and sex, meta-regression analysis was performed with age and sex separately incorporated as covariates. Age was therefore categorized into 3 age categories [“young” (18–60 y), “middle” (61–75 y), and “old” (>75 y)] and also into 2 age categories [“young” (18–60 y) and “other” (>60 y)]. Other potential sources of heterogeneity at the study level (effect modifiers) were also investigated by meta-regression analysis (Supplemental Methods).

Sensitivity analyses

The following sensitivity analyses were performed: 1) outlier analyses by omitting one study at a time, 2) omitting studies with large sample sizes (n >5000), 3) omitting studies that used the relative TL to estimate the absolute TL, 4) stratification by method of measurement of TL [Southern blot compared with quantitative polymerase chain reaction (qPCR)], and 5) using a cutoff of 90% for defining ethnicity (Supplemental Methods).

Statistical analyses were performed with the use of Stata software version 12.0 (StataCorp). All of the statistical tests were 2-sided; P values <0.05 were considered significant, except where otherwise specified.

RESULTS

Search

The search (PubMed, EMBASE, and Cochrane) yielded 5021 publications, from which 173 potentially relevant publications were identified. Some authors contributed to >1 publication. As a result, 153 corresponding authors were identified and contacted. Seventy-five corresponding authors responded positively, 69 authors did not respond, 6 declined to participate, and 3 authors did not have the requested data. Because 1 publication could include multiple studies, the PIs (if not the same as corresponding authors) of the studies were contacted. Eight additional studies were identified by the corresponding authors and the PIs of these additional studies were contacted. We decided to exclude 9 studies that used 1) techniques other than Southern blots and qPCR or 2) did not report TL in bp or T/S ratio, because the regression coefficients (β estimates) may not be directly comparable.

In total, 87 unique studies were included in the meta-analyses. Twenty-nine studies measured absolute TL and 58 studies used the T/S ratio. A flow chart of the inclusion procedure is presented in Figure 1.

FIGURE 1.

FIGURE 1

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Study inclusion flow chart. L/C ratio, telomere/centromere ratio; T/S ratio, telomere to single-copy gene ratio.

Description of studies

The characteristics of the 87 studies included in this meta-analysis are provided in Table 1. Absolute TLs were obtained from 29 studies (3, 5, 13, 14, 17, 28–61; HyperGEN study, unpublished data S. Hunt, A. Aviv, R. Cawthon 2011), of which 4 studies estimated absolute TL on the basis of the T/S ratio (19, 22, 62–68). In 17 studies, Southern blots were used (3, 5, 13, 14, 17, 28–40, 44, 45, 48–52, 55, 56, 59, 61). Fifty-eight studies presented the relative TL (T/S ratio) (4, 15, 69–133; Utah Pedigree Study, unpublished data S. Hunt, A. Aviv, R. Cawthon 2011). One PI provided the data stratified by source of leukocytes (whole blood compared with buffy coat) (104). One PI provided longitudinal data (55).

TABLE 1.

Characteristics of included study samples1

Age, n
Ref Study name All, n Men, n Women, n 18–60 y >60– 75 y >75 y Cell type Telomere length measure DNA BMI Data provided design Ethnicity (proportion white/ black/Asian/Hispanic/Native American)
Absolute telomere length measured in base pairs
 (3, 28–31) TwinsUK 3236 286 2950 2630 574 32 Leukocytes Southern blot RF Stored Measured Summary Cohort 1/0/0/0/0
 (17, 32–34) Bogalusa 635 635 0 635 0 0 Leukocytes Southern blot RF Unknown Measured Raw data Cohort 0.71/0.29/0/0/0
 (147) India CURES Study 40 20 20 37 3 0 Leukocytes Southern blot RF Stored Measured Raw data Case-control 0/0/1/0/0
 (36) Campania 528 251 277 320 100 108 Leucocytes Southern blot RF Stored Measured Raw data Cohort 1/0/0/0/0
 (14, 37–39) Asklepios 2509 1218 1291 2509 0 0 Leucocytes Southern blot RF Stored Measured Raw data Cohort 1/0/0/0/0
 (5, 13, 40) Framingham 1146 557 589 658 444 44 Leukocytes Southern blot RF NA Measured Summary Cohort 1/0/0/0/0
 (42) COPD 178 89 89 113 60 5 Leukocytes Real-time PCR NA Measured Raw data Case-control 1/0/0/0/0
 (43) Crete 109 109 0 0 0 109 Leukocytes Real-time PCR NA Measured Raw data Cohort 1/0/0/0/0
 (41) Zutphen 189 189 0 0 68 121 Leukocytes Real-time PCR NA Measured Raw data Cohort 1/0/0/0/0
 (17) Family Heart, African American 625 216 409 459 148 18 Leukocytes Southern blot RF NA Measured Summary Cohort 0/1/0/0/0
 (17) Family Heart, white 2603 1170 1433 1419 997 187 Leukocytes Southern blot RF NA Measured Summary Cohort 1/0/0/0/0
 Not published HyperGEN, African American 224 108 116 172 51 1 Leukocytes Southern blot RF NA Measured Summary Cohort 0/1/0/0/0
 Not published HyperGEN, white 1240 612 628 799 426 15 Leukocytes Southern Blot RF NA Measured Summary Cohort 1/0/0/0/0
 (44, 45) LSADT 525 171 354 0 82 443 Leukocytes Southern blot RF NA Measured Summary Cohort 1/0/0/0/0
 (46, 47) Heart and Soul 954 777 177 274 451 229 Leukocytes Real-time PCR Unknown Measured Summary Cohort 0.60/0.16/0.12/0.09/0.03
 (57, 58) Lothian 1057 530 527 0 1057 0 Leucocytes Real-time PCR Stored Measured Summary Cohort 1/0/0/0/0
 (48, 49) WarTwins 639 639 0 0 86 553 Leukocytes Southern blot RF Stored Reported Raw data Cohort 1/0/0/0/0
 (55) Jerusalem LRC 620 413 207 620 0 0 Leukocytes Southern blot RF Buffy coat stored Measured Summary Cohort 1/0/0/0/0
 (56) Jerusalem Palestinians 939 498 441 336 306 0 Leukocytes Southern blot RF Buffy coat stored Measured Summary Cross-sectional 1/0/0/0/0
 (50–52) Helsinki Businessmen Study 487 487 0 0 250 237 Leukocytes Southern blot RF Stored Reported Raw data Cohort 1/0/0/0/0
 (53) Copenhagen General Population Study 45,069 20,422 24,647 26,040 14,525 4504 Leukocytes Real-time PCR Stored Measured Summary Cohort 1/0/0/0/0
 (54) SOLVABLE 152 0 152 136 16 0 PBMCs Real-time PCR Stored Measured Summary Case-control 0.70/0.22/0.03/0.05/0
 (59) ZTL2008 25 17 8 24 1 0 PBMCs Southern blot RF Stored Measured Raw data Cohort 1/0/0/0/0
 (60) Venado Tuerto 2 401 0 401 325 63 0 Leukocytes Real-time PCR Stored Measured Raw data Cohort 0/0/0/1/0
 (61) NHSC 672 333 339 534 138 0 Leukocytes Southern blot RF Stored Measured Summary Cohort 0/0/1/0/0
Absolute telomere length estimated from T/S ratio
 (19, 62, 66, 67) South Carolina 323 145 178 305 18 0 Leukocytes Real-time PCR Unknown Measured Summary Cohort 0.57/0.41/0.01/0.01/0
 (63, 64, 68) Bruneck 800 395 405 363 315 122 Leukocytes Real-time PCR Stored Measured Summary Cohort 1/0/0/0/0
 (65) RPCI 174 0 174 111 47 16 Leukocytes Real-time PCR Stored Reported Raw data Case-control 0.93/0.05/0/0/0.02
 (22) ESTHER 3559 1583 1976 1432 2127 0 Leukocytes Real-time PCR Stored Measured Summary Cohort 1*/0/0/0/0
Telomere length based on T/S ratio
 (15) MONICA 511 183 328 419 92 0 Leukocytes Real-time PCR NA Measured Raw data Cohort 1/0/0/0/0
 (15) MDCC 476 330 146 199 277 0 Granulocytes Real-time PCR NA Measured Raw data Cohort 1/0/0/0/0
 Not published Utah Pedigree Study 964 493 471 725 183 56 Leukocytes Real-time PCR NA Measured Summary Cohort 1/0/0/0/0
 (71, 72) MESA, white 182 89 93 80 80 22 Leukocytes Real-time PCR Stored Measured Summary Cohort 1/0/0/0/0
 (71, 72) MESA, African American 278 125 153 141 109 28 Leukocytes Real-time PCR Stored Measured Summary Cohort 0/1/0/0/0
 (71, 72) MESA, Hispanic 518 252 266 245 231 42 Leukocytes Real-time PCR Stored Measured Summary Cohort 0/0/0/1/0
 (73) EARSII 395 395 0 395 0 0 Leukocytes Real-time PCR Unknown Measured Raw data Case-control 1/0/0/0/0
 (74) UCLA MacArthur 233 115 118 0 144 89 Leukocytes Real-time PCR NA Reported Summary Cohort 1/0/0/0/0
 (75) Ashkenazi 359 191 168 50 179 130 Leukocytes Real-time PCR Stored Measured Raw data Cohort 1/0/0/0/0
 (76) Warsaw 714 246 468 235 411 68 Leukocytes Real-time PCR Stored Measured Raw data Case-control 1/0/0/0/0
 (77) Finland Health 2000 cohort 938 350 588 754 137 47 Leukocytes Real-time PCR Stored Unknown Summary Cohort 1/0/0/0/0
 (78) Sister Study I (Vanguard sample) 644 0 644 475 169 0 Leukocytes Real-time PCR Stored Measured Summary Cohort 0.83/0.07/0.02/0.02/0.05
 (114) Sister Study II (Genetic Study subcohort) 734 0 734 548 186 0 Leukocytes Real-time PCR Stored Measured Summary Cohort 0.92/0.04/0/0.02/0.02
 (79) CAS 183 96 87 112 53 18 Leukocytes Real-time PCR Stored Measured Raw data Case-control 1/0/0/0/0
 (80) PATH 40 331 151 180 331 0 0 Leukocytes Real-time PCR Stored Reported Raw data Cohort 0.95/0/0.03/0/0.02
 (80) PATH 60 294 157 137 0 294 Leukocytes Real-time PCR Stored Reported Raw data Cohort 0.97/0/0.02/0/0.01
 (81) Italy alcohol controls 258 258 0 255 3 0 Leukocytes Real-time PCR Stored Reported Raw data Case-control 1/0/0/0/0
 (82) Fels Longitudinal Study 257 116 104 196 54 7 Leukocytes Real-time PCR Stored Measured Summary Cohort 1/0/0/0/0
 (83, 84) ECRAN 188 38 150 121 41 26 PBMCs Real-time PCR NA Measured Raw data Cohort 0/0/0/1/0
 (85, 86) Heart Scan Study 434 206 228 169 259 0 Leukocytes Real-time PCR Stored Measured Summary Cohort 1/0/0/0/0
 (87, 88) Boiler workers 104 104 0 97 7 0 Leukocytes Real-time PCR Stored Measured Summary Cohort 0.85/0.09/0.02/0.03/0
 (89, 90) Mayo 2886 1470 1416 2001 709 176 Leukocytes Real-time PCR NA Measured Raw data Case-control 0.98/0/0.01/0.01/0
 (91, 92, 105, 108–111) HBCS 1962 911 1051 703 1259 0 Leukocytes Real-time PCR NA Measured Raw data Cohort 1/0/0/0/0
 (93–95) PREVEND 7991 3994 3997 6094 1897 0 Leukocytes Real-time PCR NA Measured Summary Cohort 0.96/0.01/0.02/0/0.01
 (96, 97, 106, 107) Strong Heart Family Study 3256 1315 1941 2834 340 82 Leukocytes Real-time PCR NA Measured Summary Cohort 0/0/0/0/1
 (98) PREDIMED-NAVARRA 521 236 285 81 401 38 Leukocytes Real-time PCR Stored Measured Summary RCT 1/0/0/0/0
 (99) NHANES 7349 3542 3807 5034 1564 751 Leukocytes Real-time PCR Stored Measured Summary Cohort 0.52/0.18/0/0.30/0
 (100) SWHS 2912 0 2912 1812 1100 0 Leukocytes Real-time PCR NA Measured Summary Cohort 0/0/1/0/0
 (101) DHS, white 1073 493 580 821 245 7 Leukocytes Real-time PCR Stored Measured Summary Cohort 1/0/0/0/0
 (101) DHS, black 1667 606 1061 1348 317 17 Leukocytes Real-time PCR Stored Measured Summary Cohort 0/1/0/0/0
 (101) DHS, Hispanic 464 194 270 412 51 1 Leukocytes Real-time PCR Stored Measured Summary Cohort 0/0/0/1/0
 (102) DALS 734 401 333 268 366 100 Leukocytes Real-time PCR Stored Measured Summary Case-control 0.96/0/0/0.03/0
 (105, 113) FinnTwin study 2096 1101 995 1589 385 122 Leukocytes Real-time PCR NA Measured Summary Cohort 1/0/0/0/0
 (112) Former Athletes Study 586 586 0 1 376 209 Leukocytes Real-time PCR Stored Measured Summary Cohort 1/0/0/0/0
 (104) USKCS, whole blood 765 442 323 395 320 50 Leukocytes Real-time PCR Stored Measured Summary Case-control 0.61/0.39/0/0/0
 (104) USKCS, buffy coat 126 70 56 87 36 3 Leukocytes Real-time PCR Stored Measured Summary Case-control 0.66/0.34/0/0/0
 (103) Erasmus Rucphen Study 2449 1082 1367 1900 499 50 Leukocytes Real-time PCR Stored Measured Summary Case-control 1/0/0/0/0
 (103) Rotterdam Study 2231 944 1287 556 1272 404 Leukocytes Real-time PCR Stored Measured Summary Case-control 1/0/0/0/0
 (103) KORA F3 3113 1509 1604 1768 1051 294 Leukocytes Real-time PCR Stored Measured Summary Cohort 1/0/0/0/0
 (103) KORA F4 3014 1457 1557 1824 943 247 Leukocytes Real-time PCR Stored Measured Summary Cohort 1/0/0/0/0
 (148) CAVASIC 315 315 155 160 0 Leukocytes Real-time PCR Stored Measured Summary Case-control 1/0/0/0/0
 (103, 149) SAPHIR 1681 1055 626 1586 95 0 Leukocytes Real-time PCR Stored Measured Summary Cohort 1/0/0/0/0
 (115) CLHNS 3467 893 2574 3380 87 0 Leukocytes Real-time PCR Stored Measured Summary Cohort 0/0/1/0/0
 (116) NEW Study 437 0 437 304 131 2 Leukocytes Real-time PCR Stored Measured Raw data Cohort 0.85/0.08/0.02/0.03/0.02
 (117) NESDO 495 173 322 17 354 124 Leukocytes Real-time PCR Stored Measured Summary Cohort 0.95/0.01/0.04/0/0
 (118) NESDA 2936 986 1950 2749 187 0 Leukocytes Real-time PCR Stored Measured Summary Cohort 0.97/0.02/0.01/0/0
 (119) PRT 43 0 43 43 0 0 Leukocytes Real-time PCR Stored Measured Raw data Case-control **
 (120, 121) UMS 67 67 0 65 1 1 Leukocytes Real-time PCR Stored Measured Raw data Cohort 1/0/0/0/0
 (122) YMCA 1126 1126 0 1126 0 0 Leukocytes Real-time PCR Stored Measured Raw data Cohort 1/0/0/0/0
 (123–125) BASE-II 1894 946 948 441 1409 44 Leukocytes Real-time PCR Stored Measured Summary Cohort 1**/0/0/0/0
 (126) Kyiv 82 20 62 36 33 13 Leukocytes Real-time PCR Stored Measured Raw data Cohort 1/0/0/0/0
 (127) GAHR2 133 53 80 128 5 0 Leukocytes Real-time PCR Stored Measured Raw data Cohort 0.93/0.01/0.01/0.05/0
 (128) UMED telomere trial 28 7 21 26 2 0 PBMCs Real-time PCR Not stored Measured Raw data Cohort 1/0/0/0/0
 (129) ACCT 904 454 450 469 361 74 Leukocytes Real-time PCR Stored Measured Summary Cohort 0.98/0.01/0.01/0/0
 (130) RPE 975 466 509 975 0 0 Leukocytes Real-time PCR Stored Reported Raw data Cohort 0/0/1/0/0
 (131) Sweden Mindfulness Study 172 21 151 167 5 0 Leukocytes Real-time PCR Stored Reported Raw data RCT 1/0/0/0/0
 (132) EpiDREAM 4205 1565 2640 3363 770 72 Leukocytes Real-time PCR Stored Reported Summary — 0.61/0.08/0.21/0.09/0
 (133) INTERHEART 3306 2601 705 2092 997 217 Leukocytes Real-time PCR Stored Reported Summary Case-control 0.27/0.08/0.44/0.21/0
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1*Not measured, but all of Eurasian descent; **not measured. ACCT, Anglo-Cardiff Collaborative Trial; BASE-II, Berlin Aging Study; CAS, calcific aortic valve stenosis study; CAVASIC, Cardiovascular Disease in Intermittent Claudication; CLHNS, Cebu Longitudinal Health and Nutrition Survey; COPD, Chronic Obstructive Lung disease cohort; CURES, Chennai Urban Rural Epidemiology Study; DALS, Diet, Activity and Lifestyle Study; DHS, Dallas Heart Study; EARSII, European Atherosclerosis Study II; ECRAN, Envejecimiento y Enfermedades Crónicas Asociadas a Nutrición (Aging and Nutrition Associated Chronic Disease); EpiDREAM, Epidemiologic study of the Screenes for DREAM (Diabetes Reduction Assessment with Ramipril and Rosiglitazone Medication); ESTHER, Epidemiological Study on the Chances of Prevention, Early Recognition, and Optimised Treatment of Chronic Diseases in the Older Population; GAHR2, Prospective evaluation of Gender and Age differences in the impact of Hostility and Reactivity on intermediary coronary artery disease risk factors; HBCS, Helsinki Birth Cohort Study; HyperGEN, Hypertension Genetic Epidemiology Network study; KORA F3, Cooperative Health Research in the Region of Augsburg F3 (2004/2005) survey; KORA F4, Cooperative Health Research in the Region of Augsburg F4 (2006/2008) survey; LRC (Jerusalem), Lipid Research Clinic; LSADT, Longitudinal Study of Aging Danish Twins (https://www.icpsr.umich.edu/icpsrweb/NACDA/studies/21041); MDCC, Malmö Diet and Cancer Cohort; MESA, Multi-Ethnic Study of Atherosclerosis (https://www.mesa-nhlbi.org/); MONICA, Multinational Monitoring of Trends and Determinants in Cardiovascular Disease; NA, not available (data provided by principal investigator); NESDA, The Netherlands Study of Depression and Anxiety; NESDO, The Netherlands Study of Depression in older persons; NEW, Nutrition and Exercise for Women; NHSC, Nutrition and Health in Southwest China study; PATH 40, Personality and Total Health Through Life Project AGE 40-44; PATH 60, Personality and Total Health Through Life Project AGE 60–64; PBMC, peripheral blood mononuclear cell; PCR, polymerase chain reaction; PREDIMED, Prevención con Dieta Mediterránea; PREVEND, Prevention of Renal and Vascular End-stage Disease; PRT, Progressive Resistance Training; RCT, randomized controlled trial; Ref, reference; RF, restriction fragment; RPCI, Roswell Park Cancer Institute; RPE, Richard Paul Ebstein; SAPHIR, Salzburg Atherosclerosis Prevention program in subjects at High Individual Risk Study; SOLVABLE, Study of Lupus Vascular and Bone Longterm Endpoints; summary, summary statistics; SWHS, Shanghai Women's Health Study; T/S ratio, telomere to single-copy gene ratio; UMED, Uniwersytet MEDyczny w Łodzi (Medical University of Lodz (MUL)); UMS, ultra-marathon study; USKCS, US Kidney Cancer Study; YMCA, Young Men Cardiovascular Association; ZTL2008, Zannolli Telomere Length 2008.

The total pooled sample of this meta-analysis consisted of 146,114 adults (40% men), the “young” pooled sample (18–60 y) consisted of 81,446 adults (51% men), the “middle” pooled sample (61–75 y) consisted of 42,991 adults (41% men), and the “old” pooled sample (>75 y) consisted of 8495 adults (65% men). Overall, the majority of the adults were white (including Arab; 83%), followed by Asian (7%), African American (4%), and Hispanic and Native American (both 3%). Six studies provided data of mixed study populations stratified by ethnicity (17, 71, 72, 101, 132, 133; HyperGEN study, unpublished data S. Hunt, A. Aviv, R. Cawthon 2011). Sixty-five studies consisted of >70% white individuals (of which 60 had ≥90% white individuals) (3, 5, 13–15, 17, 22, 28–34, 36–45, 48–59, 63–65, 68, 71–95, 98, 100–103, 105, 108–114, 116–118, 120–129, 131; HyperGEN study, unpublished data S. Hunt, A. Aviv, R. Cawthon 2011; Utah Pedigree Study, unpublished data S. Hunt, A. Aviv, R. Cawthon 2011). Four studies consisted only of African Americans (17, 71, 72, 101; HyperGEN study, unpublished data S. Hunt, A. Aviv, R. Cawthon 2011), 5 only of Asians (35, 61, 100, 115, 130), 1 study only of Native Americans (96, 97, 106, 107), and 4 studies comprised only Hispanics (60, 71, 72, 83, 84, 101). One study could not provide information about ethnicity (119).

Assessment of small study effects

Visual inspection of the funnel plots for absolute TL and for relative TL yielded symmetric plots (Supplemental Figure 1, funnel plots). No publication bias was detected with the use of Egger's test or Begg's test. The “trim and fill” method added 1 hypothetical study to the meta-analysis for absolute TL. However, the recalculated summary estimate did not change and was still significant with its inclusion (β = −3.99; 95% CI: −5.16, −2.84; P < 0.001).

Statistical pooling

A summary of the β estimates of the meta-analysis is shown in Tables 2 and 3. An overview of the meta-analysis is shown Supplemental Tables 2 and 3 in which the β estimates of all meta-analyses for absolute TL as the outcome (Supplemental Table 2) and of all meta-analyses for relative TL as the outcome (Supplemental Table 3) are presented. The accompanying forest plots are presented in the Supplemental Figure 2.

TABLE 2.

Summary of the β estimates (regression coefficients) from the meta-analysis of the association between BMI and telomere length as the outcome and absolute telomere length (in base pairs) as the independent variable1

Pooled sample3
All (total pooled sample)2 “Young” (18–60 y old) “Middle”-aged (61–75 y old) “Old” (>75 y old)
Line Ethnicity n Estimate 95% CI I 2, % n Estimate 95% CI I 2, % n Estimate 95% CI I 2, % n Estimate 95% CI I 2, %
1 Overall 29 −3.99* −5.17, −2.81* 0.6 23 −7.67* −10.03, −5.31* 31.2 22 −1.65 −4.41, 1.11 19.7 16 −5.89* −10.41, −1.37* 5.3
2 White 21 −4.36* −5.87, −2.85* 11.3 15 −8.77* −10.42, −7.12* 0.0 15 −2.06* −4.06, −0.06* 0.0 13 −6.97* −12.29, −1.64* 15.4
3 African American 2 0.86 −4.75, 6.46 0.0 2 0.960 −5.51, 7.43 1.2 2 4.36 −7.25, 15.97 0.0 1 74.70 −76.02, 225.42
4 Hispanic 1 5.97 −149.97, 161.91 1 −45.64 −216.24, 124.95 1 212.68 −169.98, 595.34 0
5 Asian 2 −7.65 −27.20, 11.91 0.0 2 −48.70 −130.38, 32.99 10.8 1 90.00* 27.28, 152.72* 0
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1A random-effects model was used. Statistical heterogeneity was estimated by I2 statistics for each of the meta-analyses. *P < 0.05 or I2 >50%. “n” indicates number of studies.

2Adjusted for age and sex.

3Adjusted for sex.

TABLE 3.

Summary of the β estimates (regression coefficients) from the meta-analysis of the association between BMI and telomere length as the outcome and relative telomere length (T/S ratio) as the independent variable1

Pooled sample3
All (total pooled sample)2 “Young” (18–60 y old) “Middle”-aged (61–75 y old) “Old” (>75 y old)
Line Ethnicity n Estimate 95% CI I 2, % n Estimate 95% CI I 2, % n Estimate 95% CI I 2, % n Estimate 95% CI I 2, %
1 Overall 58 −1.58* −2.14, −1.01* 32.7 55 −2.58* −3.92, −1.25* 80.0* 50 −1.08* −1.76, −0.39* 0.0 29 0.20 −1.40, 1.80 0.0
2 White 43 −1.87* −2.44, −1.31* 8.1 40 −2.80* −4.77, −0.82* 84.1* 37 −1.65* −2.45, −0.86* 0.0 21 −0.28 −2.29, 1.73 0.0
3 African American 2 5.66 −6.60, 17.92 80.0* 2 5.21 −5.67, 16.08 68.7 2 0.08 −6.20, 6.36 0.0 1 −0.74 −12.62, 11.14
4 Hispanic 3 2.53 −5.18, 10.25 17.7 3 −0.42 −4.19, 3.34 0.0 3 2.31 −2.35, 6.97 0.0 2 27.29 −40.32, 94.61 77.7*
5 Asian 3 −1.11 −4.23, 2.02 60.4 3 −4.50* −5.75, −3.25* 0.0 2 2.18 −2.90, 7.27 0.0 0
6 Native American 1 −2.64* −3.60, −1.68* 1 −4.14* −5.28, −3.00* 1 2.23 −1.00, 5.46 1 4.68 −2.35, 11.71
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1The unit of the estimates and 95% CI of the T/S ratio is 10−3. A random-effects model was used. Statistical heterogeneity was estimated by I2 statistics for each of the meta-analyses. *P < 0.05 or I2 >50%.“n” indicates number of studies. T/S ratio, telomere to single-copy gene ratio.

2Adjusted for age and sex.

3Adjusted for sex.

We confirmed that age was an effect modifier. Because the associations between BMI and TL did not differ significantly between men and women, the results of the sex-specific meta-analyses are shown only in the Supplemental Results.

Overall meta-analysis

Overall, sex- and age-adjusted absolute TL was significantly associated with BMI (Table 2, line 1). Each unit increase in BMI corresponded to a −3.99 bp (95% CI: −5.17, −2.81 bp; I2 = 0.6%) difference in absolute TL (Table 2, line 1; Figure 2). For example, the estimated difference in TL between a normal-weight individual with a BMI of 25 and an obese individual with a BMI >30 is (rounded) >20.0 bp and, if a larger difference is used (BMI: 20 compared with >30), is >39.9 bp. The estimated difference between normal weight and morbid obesity (BMI >40) is >59.9 bp. Overall, each unit increase in BMI corresponded to a −1.58 × 10−3 unit T/S ratio (0.16% decrease; 95% CI: −2.14 × 10−3, −1.01 ×10−3; I2 = 41.1%) difference in age- and sex-adjusted relative TL (Table 3, line 1). The estimated difference in relative TL between normal weight and obesity is a ≥7.9 × 10−3 unit (0.79% difference) T/S ratio (Table 3, line 1; Figure 3) and between normal weight and morbid obesity is a ≥23.7 × 10−3 unit T/S ratio (2.37% difference).

FIGURE 2.

FIGURE 2

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Forest plot of the β estimates (regression coefficients) from the meta-analysis of the association between BMI and absolute telomere length (base pairs) as the outcome in the total pooled population. Random effect model was used and adjusted for age and sex. Statistical heterogeneity was estimated by I2 statistics for each of the 12 meta-analyses. The shaded boxes indicate the inverse variance weighting of each estimate, and the size of the box indicates the weight. In case no shaded box is visible, the weight is very small. COPD, Chronic Obstructive Lung disease cohort; CURES, Chennai Urban Rural Epidemiology Study; ES, estimate; ESTHER, Epidemiological Study on the Chances of Prevention, Early Recognition, and Optimised Treatment of Chronic Diseases in the Older Population; HyperGEN, Hypertension Genetic Epidemiology Network study; LRC, Lipid Research Clinic; LSADT, Longitudinal Study of Aging Danish Twins (https://www.icpsr.umich.edu/icpsrweb/NACDA/studies/21041); NHSC, Nutrition and Health in Southwest China study; RPCI, Roswell Park Cancer Institute; SOLVABLE, Study of Lupus Vascular and Bone Longterm Endpoints; ZTL2008, Zannolli Telomere Length 2008.

FIGURE 3.

FIGURE 3

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Forest plot of the β estimates (regression coefficients) from the meta-analysis of the association between BMI and relative telomere length (T/S ratio) as outcome in the total pooled population. The unit of the estimates and 95% CI is 10−3. A random-effects model was used and adjusted for age and sex. Statistical heterogeneity was estimated by I2 statistics for each of the 12 meta-analyses. The shaded boxes indicate the inverse variance weighting of each estimate, and the size of the box indicates the weight. In case no shaded box is visible, the weight is very small. ACCT, Anglo-Cardiff Collaborative Trial; BASE-II, Berlin Aging Study; CAS, calcific aortic valve stenosis study; CAVASIC, Cardiovascular Disease in Intermittent Claudication; DALS, Diet, Activity and Lifestyle Study; DHS, Dallas Heart Study; EARSII, European Atherosclerosis Study II; ECRAN, Envejecimiento y Enfermedades Crónicas Asociadas a Nutrición (Aging and Nutrition Associated Chronic Diseas); EPIDREAM, Epidemiologic study of the Screenes for DREAM (Diabetes Reduction Assessment with Ramipril and Rosiglitazone Medication); ES, estimate; GAHR2, Prospective evaluation of Gender and Age differences in the impact of Hostility and Reactivity on intermediary coronary artery disease risk factors; HBCS, Helsinki Birth Cohort Study; KORA F3, Cooperative Health Research in the Region of Augsburg F3 (2004/2005) survey; KORA F4, Cooperative Health Research in the Region of Augsburg F4 (2006/2008) survey; MDCC, Malmö Diet and Cancer Cohort; MESA, Multi-Ethnic Study of Atherosclerosis (https://www.mesa-nhlbi.org/); MONICA, Multinational Monitoring of Trends and Determinants in Cardiovascular Disease; NESDA, The Netherlands Study of Depression and Anxiety; NESDO, The Netherlands Study of Depression in older persons; NHANES, National Health and Nutrition Examination Survey; Not publ, not published; PATH 40, Personality and Total Health Through Life Project AGE 40-44; PATH 60, Personality and Total Health Through Life Project AGE 60-64; PREDIMED, Prevención con Dieta Mediterránea; PREVEND, Prevention of Renal and Vascular End-stage Disease; PRT, Progressive Resistance Training; Ref, reference; RPE, Richard Paul Ebstein; SAPHIR, Salzburg Atherosclerosis Prevention program in subjects at High Individual Risk Study; SWHS, Shanghai Women's Health Study; T/S ratio, telomere to single-copy gene ratio; UMED, Uniwersytet MEDyczny w Łodzi (Medical University of Lodz (MUL)); UMS, ultra-marathon study; USKCS, US Kidney Cancer Study; YMCA, Young Men Cardiovascular Association.

Meta-analysis stratified by age categories

Analysis stratified by age category showed that in “young” adults (18–60 y) a unit increase in BMI corresponded to a −7.67 bp (95% CI: −10.03, −5.31 bp; I2 = 31.2%) difference in absolute TL (Table 2, line 1; Figure 4).

FIGURE 4.

FIGURE 4

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Forest plot of the β estimates (regression coefficients) from the meta-analysis of the association between BMI and absolute telomere length (base pairs) as outcome in the “young” pooled population (age ≥18 and ≤60 y). A random-effects model was used and adjusted for sex. Statistical heterogeneity was estimated by I2 statistics for each of the 12 meta-analyses. The shaded boxes indicate the inverse variance weighting of each estimate, and the size of the box indicates the weight. In case no shaded box is visible, the weight is very small. COPD, Chronic Obstructive Lung disease cohort; CURES, Chennai Urban Rural Epidemiology Study; ES, estimate; ESTHER, Epidemiological Study on the Chances of Prevention, Early Recognition, and Optimised Treatment of Chronic Diseases in the Older Population; HyperGEN, Hypertension Genetic Epidemiology Network study; LRC, Lipid Research Clinic; LSADT, Longitudinal Study of Aging Danish Twins (https://www.icpsr.umich.edu/icpsrweb/NACDA/studies/21041); NHSC, Nutrition and Health in Southwest China study; Not publ, not published; Ref, reference; RPCI, Roswell Park Cancer Institute; SOLVABLE, Study of Lupus Vascular and Bone Longterm Endpoints; ZTL2008, Zannolli Telomere Length 2008.

In “middle”-aged adults (61–75 y) the overall association between BMI and TL was −1.65 bp (95% CI: −4.41, 1.11 bp; I2 = 19.7) per unit increase in BMI. In “old” adults (>75 y) the overall association between BMI and TL was −5.89 bp (95% CI: −10.41, −1.37 bp; I2 = 5.3) per unit increase in BMI (Table 2, line 1).

For relative TL, each unit increase in BMI corresponded to a −2.58 × 10−3 unit T/S ratio (0.26% decrease; 95% CI: −3.92 × 10−3, −1.25 × 10−3; I2 = 80.0%) difference in relative TL in young adults (Table 3, line 1; Figure 5).

FIGURE 5.

FIGURE 5

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Forest plot of the β estimates (regression coefficients) from the meta-analysis of the association between BMI and relative telomere length (T/S ratio) as the outcome in the “young” pooled population (age ≥18 y and ≤60 y). The unit of the estimates and 95% CI is 10−3. A random-effects model was used and adjusted for sex. Statistical heterogeneity was estimated by I2 statistics for each of the 12 meta-analyses. The shaded boxes indicate the inverse variance weighting of each estimate, and the size of the box indicates the weight. In case no shaded box is visible, the weight is very small. ACCT, Anglo-Cardiff Collaborative Trial; BASE-II, Berlin Aging Study; CAS, calcific aortic valve stenosis study; CAVASIC, Cardiovascular Disease in Intermittent Claudication; DALS, Diet, Activity and Lifestyle Study; DHS, Dallas Heart Study; EARSII, European Atherosclerosis Study II; ECRAN, Envejecimiento y Enfermedades Crónicas Asociadas a Nutrición (Aging and Nutrition Associated Chronic Diseas); EpiDREAM, Epidemiologic study of the Screenes for DREAM (Diabetes Reduction Assessment with Ramipril and Rosiglitazone Medication); ES, estimate; GAHR2, Prospective evaluation of Gender and Age differences in the impact of Hostility and Reactivity on intermediary coronary artery disease risk factors; HBCS, Helsinki Birth Cohort Study; KORA F3, Cooperative Health Research in the Region of Augsburg F3 (2004/2005) survey; KORA F4, Cooperative Health Research in the Region of Augsburg F4 (2006/2008) survey; MDCC, Malmö Diet and Cancer Cohort; MESA, Multi-Ethnic Study of Atherosclerosis (https://www.mesa-nhlbi.org/); MONICA, Multinational Monitoring of Trends and Determinants in Cardiovascular Disease; NESDA, The Netherlands Study of Depression and Anxiety; NESDO, The Netherlands Study of Depression in older persons; Not publ, not published; PATH 40, Personality and Total Health Through Life Project AGE 40–44; PREDIMED, Prevención con Dieta Mediterránea; PREVEND, Prevention of Renal and Vascular End-stage Disease; PRT, Progressive Resistance Training; Ref, reference; RPE, Richard Paul Ebstein; SAPHIR, Salzburg Atherosclerosis Prevention program in subjects at High Individual Risk Study; SWHS, Shanghai Women's Health Study; T/S ratio, telomere-to–single-copy gene ratio; UMED, Uniwersytet MEDyczny w Łodzi (Medical University of Lodz (MUL)); UMS, ultra-marathon study; USKCS, US Kidney Cancer Study; YMCA, Young Men Cardiovascular Association.

In “middle”-aged adults, the overall association between BMI and relative TL was found to be a −1.08 × 10−3 unit T/S ratio (0.1% decrease; 95% CI: −1.76 × 10−3, −0.39 × 10−3; I2 = 0.0) per unit increase in BMI. For “old” adults no significant association [0.20 × 10−3 unit T/S ratio (0.02%); 95% CI: −1.40 × 10−3, 1.80 × 10−3; I2 = 0.0] was found between BMI and relative TL (Table 3, line 1).

Meta-regression and sources of heterogeneity

Age and ethnicity were effect modifiers in the meta-regression analyses (Supplemental Results). Therefore, all of the analyses were stratified by ethnicity in addition to the originally planned analyses.

With absolute TL as the outcome, stratified analyses showed that all estimates were significant for the white pooled sample (Table 2, line 2) and also for the “middle” pooled Asian sample. However, the latter estimate was an outlier and based on just 1 study (Table 2, line 5).

With relative TL as the outcome, stratified analyses showed that the estimates (except for one estimate for the “old” pooled sample) were significant for the white pooled sample (Table 3, line 2). In addition, the estimates of the overall and “young” pooled Native American samples and the estimate of the “young” pooled Asian sample were significant (Table 3, lines 5 and 6).

Sensitivity analysis

None of the sensitivity analyses resulted in a substantial change in the summary estimate (Supplemental Results).

DISCUSSION

This cross-sectional meta-analysis of 87 observational studies of adult pooled populations confirmed previous observations that BMI is negatively associated with TL. After stratification for age and ethnicity, the negative association between BMI and TL appeared to be stronger in the “young” pooled population (18– 60 y) and in the white pooled population. Differences between men and women could not be confirmed.

On the basis of our estimates for absolute TL, an ∼5-unit higher BMI appears to be equivalent to a difference in TL of ∼20–38 bp or an ∼7.9 × 10−3 to 13 × 10−3 unit T/S ratio (0.79–1.3% difference). Compared with an estimated average yearly decrease (i.e., ∼25 bp/y or ∼0.01 T/S ratio/y) in leucocyte TL in adults based on cross-sectional data (3, 32, 134–136), this is equivalent to an increase in biological age of ∼1 y. A major disadvantage of cross-sectional analysis is the impossibility to infer causation. However, the robust association between higher BMI and lower TL found in this meta-analysis could highlight another potential area of concern for the obesity epidemic.

Because obesity and, more specifically, an increase in leptin and a decrease in adiponectin have been associated with low-grade inflammation and oxidative stress (137), the observed negative association between BMI and leukocyte TL may be due, in part, to the chronic inflammatory state associated with higher leptin. Recently, a negative association was observed between age-related relative TL and serum leptin in 7 cohorts of 11,448 participants, which remained significant after adjustment for BMI (103). These data suggest that, beyond a high BMI, inflammatory conditions, mediated via increase in leptin, likely contribute to telomere shortening. Because a longitudinal intervention study showed that a reduction in BMI was linked to increasing TL over a 5-y period (98), it is also suggested that a common factor, such as chronic inflammation, is associated both with leptin resistance and with TL.

The negative association between BMI and TL was most apparent in the younger pooled population, in whom a stronger association was found for TL than in the other age groups. Three possible explanations could explain this observation. First, BMI could be a better marker for adiposity in younger individuals aged <60 y than in older individuals (22). Above 65 y of age BMI may less consistently reflect obesity because of potential loss of muscle and bone mass and height (22). The fact that older men weighed less than the middle-aged men at a given height is attributed to older men having less lean tissue, and a lower BMI can actually reflect a higher fat mass (138). Second, selective survival might be one of the causes of the stronger association found in the younger age category. As Manson et al. (139) stated, “obesity in one's 40s contributes to the onset of type 2 diabetes in one's 50s, which leads to myocardial infarction (MI) in one's 60s, heart failure and weight loss due to debilitation and muscle wasting at age 70, and death at age 75”. People who suffered from age-related diseases may have died and those who survived may therefore differ from those who died (140). Third, older people are more likely to have chronic diseases that lead to weight loss and people with chronic diseases are probably less likely to participate in studies (139).

The magnitude of the negative association between BMI and leukocyte TL was found to be largest in the white pooled population. One possible explanation could be that TL differs between different cell types (141) and that leukocyte cell subpopulations (142) differ between whites and African Americans. However, because only 4 samples consisted of African Americans, more research is required to resolve whether this observation explains the racial differences in association between TL and BMI for white and African Americans or whether this is a false-positive finding. Second, it was reported that the estimation of visceral adipose tissue, the most relevant tissue that determines the risk of developing chronic metabolic diseases, was different in white and African American adults (143). At a higher BMI or increased waist circumference, white adults had higher amounts of visceral adipose tissue than African-American adults (137). Because the presence of leptin resistance or markers of inflammation was not included in these studies, it remains to be determined whether the relation between BMI, leptin resistance, inflammation, and telomere attrition is different for African Americans compared with whites. In addition, the one study sample consisting of 3256 mostly “young” Native Americans showed similar results as found for the white study population (107). The majority of this study sample was centrally obese, and leukocyte TL was negatively correlated with C-reactive protein.

One of the main strengths of this study is that we did not rely on publications only. Instead, we contacted PIs, which, in turn, pointed us toward important studies we may have missed to obtain the data used in the meta-analysis. In addition, we incorporated potential confounders (age and sex) and sources of heterogeneity (ethnicity and study design). The response rate of the originally contacted PIs was 55%, with a final count of 87 unique studies and >140,000 individuals. Although it is impossible to make a direct comparison with the unpublished β estimates of the nonresponders, we assume, also based on the absence of significant publication bias, that the studies in this meta-analysis are a random selection of all studies conducted and that we present a valid representation of the association between BMI and TL. Because of the large variation in adult TL, as well as biological and measurement variation (qPCR), large sample sizes are needed, especially in cross-sectional studies, to detect modest effects (30). In this meta-analysis we were able to detect a significant association of −3.99 bp or a −1.58 × 10−3 unit T/S ratio (0.16%) per unit increase in BMI. Because 36% of our meta-analyses showed a significant association with estimates in the same direction and of the same magnitude (except for 3 estimates of extreme magnitude of one Asian sample in the “middle”-aged population), we assume that false positive reporting is only of minor concern.

Two recent meta-analyses, which relied on published data, also reported negative associations between BMI and TL. The first smaller scale meta-analysis reported negative regression coefficients on the association between TL and BMI (22), of which 5 studies were also included in this meta-analysis (13, 14, 19, 82, 144). The larger-scale meta-analysis reported a weak negative correlation, a standardized mean difference of 0.84 (95% CI: 0.22, 1.46) between obese individuals and normal-weight individuals, and an OR of 1.39 (95% CI: 1.15, 1.69) (7). Of the 45 samples that met our inclusion criteria 33 collaborated in our analysis. This shows that the results between the meta-analyses are consistent and very robust.

Although age and ethnicity were taken into account, it should be mentioned that the older study sample was relatively small (∼8400 individuals) and that the majority of the individuals were white (83%). Unfortunately, we did not include smoking in the meta-analysis. Smoking is generally associated with a lower BMI and shorter TL (3, 139, 145), which may have caused an underestimation of the inverse association between BMI and TL. In addition, inflammation was not directly measured. We were also not able to measure telomere attrition because we did not incorporate longitudinal data, and reverse causation cannot be excluded. However, there are very few large-scale studies with repeated measures of TL.

The lengths of telomeres at different ages are highly correlated, and it has been suggested that most of the variation in leukocyte TL in adults is a result of TL at birth and that therefore the impact of environmental and lifestyle factors is rather small (145, 146). In addition, Benetos et al. (145) described that ranking of individuals into deciles according to their TL barely changes across adult life. Our meta-analysis shows that a 5-unit increase in BMI corresponds to a change of ∼20 bp or even ∼38 bp in the younger pooled population, which is equivalent to 1-y greater biological age, irrespective of ranking.

In summary, a higher BMI is associated with shorter telomeres, especially in the younger pooled population. Being aware of the fact that the association between BMI and TL differs across the life span can lead to further research. Although no causal inference can be drawn and residual confounding may exist, the results were robust across a variety of potential confounders. Given this, we could possibly infer that tackling the obesity epidemic might be a starting point to delay telomere shortening and the onset of age-related diseases, thereby contributing to slower biological aging of the population. However, meta-analyses of longitudinal studies that can evaluate change in body weight alongside change in TL are warranted.

Supplementary Material

Supplemental Material
Click here for additional data file. (392.5KB, pdf)

Acknowledgements

The collaborators in the TELOMAAS group are as follows: Göran Roos (Department of Medical Biosciences, Umeå University, Umeå, Sweden); Birgitta Stegmayr (Department of Public Health and Clinical Medicine, Umeå University, Sweden); Peter Nilsson (Clinical Sciences Medicine, University Hospital, Malmö, Sweden); Mats Eliasson (Department of Medicine, Sunderbyn Hospital, Luleå, Sweden); Olle Melander (Department of Clinical Sciences Medicine, University Hospital, Malmö, Sweden); Tim Spector (Twin Research and Genetic Epidemiology, King's College London, United Kingdom); Abraham Aviv (Center of Human Development and Aging, New Jersey Medical School, University of Medicine and Dentistry of New Jersey, Rutgers The State University of New Jersey, Newark, NJ); Antonysunil Adaikalakoteswari (Division of Metabolic and Vascular Health, University of Warwick, Coventry, United Kingdom); Viswanathan Mohan (Department of Diabetology, Madras Diabetes Research Foundation and Dr Mohans’ Diabetes Specialities Center, Gopalapuram, Chennai, India); Sofie Bekaert (Bimetra Clinical Research Center Ghent, Ghent University Hospital, Ghent, Belgium); Denil Simon (Department of Mathematical Modeling, Statistics, and Bioinformatics, Ghent University, Ghent, Belgium); Ernst Rietzschel, Marc De Buyzere, Thierry Gillebert, and Caroline Van Daele (Department of Cardiovascular Diseases, Ghent University Hospital, Ghent, Belgium); Michel Langlois (Department of Clinical Chemistry, AZ St-Jan Brugge-Oostende AV, Bruges, Belgium); Patrick Segers (Institute Biomedical Technology, Ghent University, Ghent, Belgium); Gui De Backer and Dirk De Bacquer (Department of Public Health, Ghent University, Ghent, Belgium); Michael Fenech (Department of Animal Food and Health Sciences, CSIRO, Adelaide BC, Australia); David Hughes and Janet Cross (Institute of Food Research, Norwich, United Kingdom); Helen Murphey (Department of Diabetes and Endocrinology, Ipswich Hospital, Ipswich, United Kingdom); Rosemary C Temple (Elsie Bertram Diabetes Center, Norfolk and Norwich University Hospital NHS Trust, Norwich, United Kingdom); Duncan Fowler (Department of Diabetes and Endocrinology, Ipswich Hospital, Ipswich, United Kingdom); Nicoletta Dozio (Elsie Bertram Diabetes Center, Norfolk and Norwich University Hospital NHS Trust, Norwich, United Kingdom); Jackie Hughes and Mark Winterbone (Institute of Food Research, Norwich, United Kingdom); Shi-Jen Hwang and Daniel Levy [Population Sciences Branch of the National Heart, Lung, and Blood Institute (NHLBI), NIH, NHLBI's Framingham Heart Study, Framingham, MA]; Daan Kromhout (Division of Human Nutrition, Wageningen University, Wageningen, Netherlands); Anthony Kafatos (Department of Preventive Medicine and Nutrition Clinic, Medical School, University of Crete, Heraklion, Crete, Greece); Richard M Cawthon (Eccles Institute of Human Genetics, University of Utah, Salt Lake City, UT); Ana V Diez-Roux (Drexel University School of Public Health, Philadelphia, PA); Steve E Humphries (Department of Cardiovascular Genetics, Institute Cardiovascular Science, University College London, London, United Kingdom); Arch G Mainous III (Department of Health Services Research, Management, and Policy, University of Florida, Gainesville, FL); Marty S Player (Department of Family Medicine, Medical University of South Carolina, Charleston, SC); Charles J Everett (Ralph H Johnson VA Medical Center, Charleston, SC); Nir Barzilai (Department of Medicine and Genetics, Albert Einstein College of Medicine, Bronx, NY); Ian J Deary (Center for Cognitive Ageing and Cognitive Epidemiology and Department of Psychology, University of Edinburgh, Edinburgh, United Kingdom); John M Starr (Center for Cognitive Aging and Cognitive Epidemiology and Alzheimer Scotland Dementia Research Center, University of Edinburgh, Edinburgh, United Kingdom); Thomas von Zglinicki (Institute for Cell and Molecular Biosciences and Newcastle University Institute for Aging, Campus for Aging and Vitality, Newcastle upon Tyne, United Kingdom); Carmen Martin-Ruiz (NIHR Biomedical Research Center at Newcastle upon Tyne, Institute of Neuroscience and Newcastle University Institute for Aging, Campus for Aging and Vitality, Newcastle upon Tyne, United Kingdom); Nazmul Huda (Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN); Laura Kananen (Research Programs Unit, Biomedicum-Helsinki, University of Helsinki, Helsinki, Finland); Ronit Sinnreich (Epidemiology Unit, Hebrew University-Hadassah School of Public Health and Community Medicine, Jerusalem, Israel); Barbara Kloeckener-Gruissem (Institute of Medical Molecular Genetics, University of Zurich, Schlieren, Switzerland, and Department of Biology, ETHZ, Zurich); Simon Easteal and Peter J Milburn (John Curtin School of Medical Research, The Australian National University, Canberra, Australia); Stefan Kiechl and Johann Willeit (Department of Neurology, Medical University Innsbruck, Innsbruck, Austria); Julia Raschenberger (Division of Genetic Epidemiology, Department of Medical Genetics, Molecular and Clinical Pharmacology, Medical University of Innsbruck, Innsbruck, Austria); Susan McCann and Christine Ambrosone (Department of Cancer Prevention and Control, Roswell Park Cancer Institute, Buffalo, NY); Andrea Baccarelli (Department of Environmental Health Sciences, Columbia Mailman School of Public Health, New York, NY); Mirjam Hoxha (Department of Clinical Sciences and Community Health, EPIGET– Epidemiology, Epigenetics and Toxicology Lab, University of Milan, Milan, Italy); Pier Alberto Bertazzi (Department of Clinical Sciences and Community Health, University of Milan and IRCCS Ca Granda Maggiore Hospital Foundation, Milan, Italy); Santo Davide Ferrara and Massimo Montisci (Department of Cardiac, Thoracic, and Vascular Sciences, Unit of Legal Medicine and Forensic Toxicology, University of Padova, Padova, Italy); Miryoung Lee (Community Health, Lifespan Health Research Center, Wright State University Boonshoft School of Medicine, Dayton, OH); Stig E Bojesen and Børge G Nordestgaard (The Copenhagen General Population Study, Department of Clinical Biochemistry, Copenhagen University Hospital, Herlev and Gentofte Hospital, Copenhagen, Denmark); Gladys Barrera, Sandra Hirsch, and María Pía De la Maza (Institute of Nutrition and Food Technology, University of Chile, Chile); Halcyon Skinner (Truven Health, Durham, NC); Kristin Litzelman (National Cancer Institute, Division of Cancer Control and Population Sciences, Rockville, MD); Songwon Seo (Korea Institute of Radiological and Medical Sciences, National Radiation Emergency Medical Center, Seoul, South Korea); Sonia Garcia-Calzon (Department of Nutrition, Food Science, and Physiology, University of Navarra, Pamplona, Spain, and Instituto de Investigación Sanitaria de Navarra, Pamplona, Spain); Guillermo Zalba (Department of Biochemistry and Genetics, University of Navarra, Pamplona, Spain, and Instituto de Investigación Sanitaria de Navarra, Pamplona, Spain); J Alfredo Martinez and Miguel Angel Martinez-Gonzalez [Department of Nutrition, Food Science, and Physiology, University of Navarra, Pamplona, Spain, and Instituto de Investigación Sanitaria de Navarra, Pamplona, Spain, and CIBER Fisiopatología de la Obesidad y Nutrición, (CIBERobn), Instituto de Salud Carlos III, Madrid, Spain]; David Rehkopf (Department of General Medical Disciplines, Stanford University, Stanford, CA); Jue Lin (Department of Biochemistry and Biophysics, University of California, San Francisco, CA); Yu-Tang Gao (Department of Epidemiology, Shanghai Cancer Institute, Shanghai, China); Xiao-Ou Shu (Department of Medicine, Division of Epidemiology, Vanderbilt University Medical Center, Nashville, TN); Carly Skamra, Alexander Sandhu, and QiQuan Huang (Division of Rheumatology, Northwestern University Feinberg School of Medicine, Chicago, IL); Jungwha Lee (Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL); Richard Pope (Division of Rheumatology, Northwestern University Feinberg School of Medicine, Chicago, IL); Martha Slattery (Department of Medicine, University of Utah, Salt Lake City, UT); Merja K Laine (Department of General Practice and Primary Health Care, University of Helsinki, Helsinki, Finland, and Vantaa Health Center, Vantaa, Finland); Johan G Eriksson (Department of General Practice and Primary Health Care, University of Helsinki, Helsinki, Finland); Urho M Kujala (Department of Health Sciences, University of Jyväskylä, Jyväskylä, Finland); Rahul Raj (Department of Neurosurgery, Helsinki University Central Hospital, Helsinki, Finland); Heli M Bäckmand (City of Vantaa Health and Social Welfare Department, Vantaa, Finland); Markku Peltonen (Department of Health, National Institute for Health and Welfare, Helsinki, Finland); Seppo Sarna (Department of Public Health, University of Helsinki, Helsinki, Finland); Kendra Schwartz (Department of Family Medicine and Public Health Sciences, Wayne State University, Detroit, MI); Faith Davis (School of Public Health, University of Alberta, Alberta, Canada); Julie Ruterbusch (Department of Family Medicine and Public Health Sciences, Wayne State University, Detroit, MI); Mirjam Hoxha (Department of Occupational and Environmental Health, University of Milan, Milan, Italy); Bridget McCarthy (School of Public Health, University of Illinois at Chicago, Chicago, IL); Nathaniel Rothman and Joanne Colt (Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD); Wong-Ho Chow (Department of Epidemiology, MD Anderson Cancer Center, Houston, TX); Mark Purdue (Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD); Clemens Baumbach (Research Unit of Molecular Epidemiology and Institute of Epidemiology II, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany); Annette Peters (Institute of Epidemiology II, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany); Martina Müller-Nurasyid (Institute of Genetic Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany, and Department of Medicine I, Ludwig-Maximilians-University Munich, Munich, Germany, and DZHK (German Centre for Cardiovascular Research), partner site Munich Heart Alliance, Munich, Germany); Michelangela Barbieri (Department of Medical, Surgical, Neurological, Metabolic, and Geriatric Sciences, Second University of Naples, Naples, Italy); Kaare C Christensen (Department of Epidemiology, Biostatistics, and Biodemography, Institute of Public Health, University of Southern Denmark, Odense C, Denmark); Anu Loukola (Department of Public Health, University of Helsinki, Helsinki, Finland); Tellervo Korhonen (Department of Public Health, University of Helsinki, Finland, and University of Eastern Finland, Kuopio, Finland); Pamela AF Madden (Washington University School of Medicine, St. Louis, MO); Sharon Merkin (Department of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA); Katri Räikkönen (Institute of Behavioural Sciences, University of Helsinki, Helsinki, Finland); Eero Kajantie (Department of General Practice and Primary Health Care, University of Helsinki, Helsinki, Finland); Johan Eriksson (Department of General Practice and Primary Health Care and Helsinki University Hospital, University of Helsinki, Helsinki, Finland); Pascal Arp (Department of Internal Medicine, Erasmus MC, Rotterdam, Netherlands); Albert Hofman and Cornelia M van Duijn (Department of Epidemiology, Erasmus MC, Rotterdam, Netherlands); André G Uitterlinden (Department of Internal Medicine, Erasmus MC, Rotterdam, Netherlands); Lyudmyla Kedenko and Bernhard Paulweber (Department of Internal Medicine I, Paracelsus Medical University, Salzburg, Austria); Margot Haun and(Division of Genetic Epidemiology, Department of Medical Genetics, Molecular and Clinical Pharmacology, Medical University of Innsbruck, Innsbruck, Austria); Barbara Rantner (Division of Genetic Epidemiology, Departments of Medical Genetics, Molecular and Clinical Pharmacology, and Vascular Surgery, Medical University of Innsbruck, Innsbruck, Austria); Angelika Hammerer-Lercher (Central Institute of Medical and Chemical Laboratory Diagnostics, University Hospital Innsbruck, Innsbruck, Austria); Gustav Fraedrich (Department of Vascular Surgery, Innsbruck Medical University, Innsbruck, Austria); Marietta Stadler (Third Medical Department of Metabolic Diseases and Nephrology, Hietzing Hospital, Vienna, Austria); Peter Klein-Weigel (Clinic for Angiology, HELIOS Klinikum Berlin-Buch, Berlin, Germany); M Geoffrey Hayes (Division of Endocrinology, Metabolism, and Molecular Medicine, Department of Medicine, and Center for Genetic Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, and Department of Anthropology, Northwestern University, Evanston, IL); Christopher W Kuzawa (Department of Anthropology, Northwestern University, Evanston, IL, and Cells to Society (C2S), The Center on Social Disparities and Health, Institute for Policy Research, Northwestern University, Evanston, IL); Nanette Lee (Office of Population Studies Foundation, University of San Carlos, Cebu, Philippines); Caitlin Mason (Fred Hutchinson Cancer Research Center, Seattle, WA); Rosa-Ana Risques and Peter Rabinovitch (Department of Pathology, University of Washington, Seattle, WA); Ching-Yun Wang (Fred Hutchinson Cancer Research Center, Seattle, WA); Anne McTiernan (Fred Hutchinson Cancer Research Center, Seattle, WA, and Schools of Public Health and Medicine, University of Washington, Seattle, WA); Cristiana LMF Miranda-Furtado (Department of Gynecology and Obstetrics, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, São Paulo, Brazil); Hermann Brenner [Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany, and Network Aging Research (NAR), University of Heidelberg, Heidelberg, Germany]; Antje Meyer [Center of Stroke Research Berlin (CSB) and Berlin-Brandenburg Center for Regenerative Therapies (BCRT), at Campus Virchow Klinikum, Charité Universitätsmedizin, Berlin, Germany]; Elisabeth Steinhagen-Thiessen (Charité–Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Lipid Clinic at the Interdisciplinary Metabolism Center, Germany); Julieta Panero and LaPatricia Carolina Dos Santos [Academia Nacional de Medicina (CONICET), Instituto de Medicina Experimental (IMEX), Laboratorio de Genética de Neoplasias Linfoides, Ciudad Autónoma de Buenos Aires, Argentina]; Claudio Daniel González (Universidad de Buenos Aires, Facultad de Medicina, Departamento de Farmacología, Ciudad Autónoma de Buenos Aires, Argentina); Jorge Vilariño (Hospital FLENI, Departamento de Cardiología, Ciudad Autonoma de Buenos Aires, Argentina); Martha Sereday (Hospital Fiorito, Servicio de Endocrinología, Buenos Aires, Argentina); Gloria Edith Cerrone (Universidad de Buenos Aires, Facultad de Farmacia y Bioquímica, Cátedra de Genética y Biología Molecular, Ciudad Autónoma de Buenos Aires, Argentina); Irma Slavutsky [Academia Nacional de Medicina (CONICET), Instituto de Medicina Experimental (IMEX), Laboratorio de Genética de Neoplasias Linfoides, Ciudad Autónoma de Buenos Aires, Argentina]; Gustavo Daniel Frechtel [CONICET-Universidad de Buenos Aires. Instituto de Inmunología, Genética y Metabolismo (INIGEM), Laboratorio de Diabetes y Metabolismo, Ciudad Autónoma de Buenos Aires, Argentina]; Dariusz Nowak (Department of Clinical Physiology, Medical University of Lodz, Lodz, Poland); Makandjou-Ola Eusebio and Miroslawa Pietruczuk (Department of Laboratory Diagnostics, II Department of Internal Medicine, Medical University of Lodz, Lodz, Poland); Yasmin (Division of Experimental Medicine and Immunotherapeutics, University of Cambridge, Cambridge, United Kingdom); Lee Butcher (Cardiff School of Sport and Health Sciences, Cardiff Metropolitan University, Cardiff, United Kingdom); John R Cockcroft (Division of Cardiology, New York–Presbyterian Hospital, Columbia University, New York, NY); Ian B Wilkinson (Division of Experimental Medicine and Immunotherapeutics, University of Cambridge, Cambridge, United Kingdom); Jorge D Erusalimsky (Cardiff School of Sport and Health Sciences, Cardiff Metropolitan University, Cardiff, United Kingdom); Carmel M McEniery (Division of Experimental Medicine and Immunotherapeutics, University of Cambridge, Cambridge, United Kingdom); Onn Siong Yim (Department of Psychology, National University of Singapore, Singapore); Xing Zhang (Singapore-ETH Center for Global Environmental Sustainability, Singapore); Idan Shalev (Department of Biobehavioral Health, Pennsylvania State University, University Park, PA); Mikhail Monakhov (UConn Health, Neuroscience, Farmington, CT); Songfa Zhong (Department of Economics, National University of Singapore, Singapore); Ming Hsu (University of California/Haas School of Business, Berkeley, CA); Soo Hong Chew (Department of Economics, National University of Singapore, Singapore); Poh San Lai (Department of Pediatrics, National University of Singapore, Singapore); Jan Sundquist and Xiao Wang (Center for Primary Health Care Research, Lund University, Region Skåne, Lund, Sweden); Salim Yusuf, Hertzel Gerstein, and Sonia Anand (Population Health Research Institute and McMaster University, Hamilton, Canada).

We thank the principal investigators of the Bogalusa Study, the Sister Study, the Strong Heart Family Study, and the Helsinki Birth Cohort Study (HBCS) for their willingness to share the data but with whom we lost contact despite our efforts.

The TwinsUK study was funded by the Wellcome Trust, European Community's Seventh Framework Programme (FP7/2007–2013). The study also receives support from the National Institute for Health Research (NIHR) BioResource Clinical Research Facility and Biomedical Research Centre based at Guy's and St. Thomas' NHS Foundation Trust and King's College London. The Dallas Heart Study (DHS) research reported in this publication was supported by the National Center for Advancing Translational Sciences of the NIH under award number UL1TR001105. We thank Drs. C Garcia and Kozlitina for sharing the data. The acquisition and analyses of the data from the Multi-Ethnic Study of Atherosclerosis (MESA) was funded by contracts N01-HC-95159 through N01-HC-95165 and N01-HC-95169 from the National Heart, Lung, and Blood Institute, by R01 HL076831 (PI: Diez Roux), and by funding from the MacArthur Foundation. Jennifer A Nettleton was funded by a career development award from the NIH, the National Institute of Diabetes and Digestive and Kidney Diseases (5K01DK082729-04). MESA thanks the investigators, staff, and participants of the MESA study for their valuable contributions. A full list of participating MESA investigators and institutions can be found at http://www.mesa-nhlbi.org. SE Humphries is a British Heart Foundation Professor and he and Klelia D Salpea are supported by the British Heart Foundation (RG008/08 and FS/06/053) and by the National Institute for Health Research University College London Hospitals Biomedical Research Centre. The EARSII study was funded by the EC Concerted Action BMH1 CT92-0206. Phenotype collection was supported by Age UK (The Disconnected Mind project). The work was undertaken by The University of Edinburgh Centre for Cognitive Ageing and Cognitive Epidemiology, which is part of the cross-council Lifelong Health and Wellbeing Initiative (MR/K026992/1). Funding from the Biotechnology and Biological Sciences Research Counci and Medical Research Council (MRC) is gratefully acknowledged. The Sister Study was supported by the Intramural Research Program of the NIH, National Institute of Environmental Health Sciences (Z01 ES 044005). The Jerusalem LRC study was funded by the Chief Scientist of the Israel Ministry of Health (300000-5352), the Israel Science Foundation (593/01), and the US-Israel Binational Science Foundation (87-00419). The Jerusalem Palestinians study was funded by the USAID MERC program (grant TA-MOU-01-M21–002) and by a research grant from DCURE Israel. The PATH study was supported by the Australian National Health and Medical Research Council program grant 17805 and project grant 418020. The Bruneck Study was supported by a grant from the “Tiroler Wissenschaftsfonds,” Austria, and the Dr Johannes and Hertha Tuba Foundation. The Fels Longitudinal Study, Research reported in this manuscript was supported by the NIH grants (R03AG023251, R01HD012252).

The authors thank the Indian Health Service facilities. The views expressed in this article are those of the authors and do not necessarily reflect those of the Indian Health Service. This study was supported by NIH grants R01DK091369, K01AG034259, R21HL092363, and cooperative agreement grants U01HL65520, U01HL41642, U01HL41652, U01HL41654, and U01HL65521.

SOLVABLE was supported by NIH/NIAMS T32AR07611, K24AR002138, and P60 AR30692, which is now updated as P60 AR064464, Kirkland Scholar Award, and the AFMR Summer Clinical and the Eleanor Wood-Prince grant: A Project of the Woman's Board of Northwestern Memorial Hospital. The Former Athletes study was funded by the Ministry of Education and Culture, the Juho Vainio Foundation, the Finnish Heart Research Foundation, Paavo Nurmi Foundation, the Finnish Cultural Foundation, and by a grant from Medical Society of Finland, Finska Läkaresällskapet. We thank National Institute for Health and Welfare, Department of Public Health, University of Helsinki, Sports and Exercise Medicine Department of Health Sciences, University of Jyväskylä, Paavo Nurmi Centre, Turku, and ORTON Research Institute, Invalid Foundation, Helsinki, for collaboration during the large epidemiological and clinical research program in the year 2008. The Finnish Twin Cohort data come primarily from the Nicotine Addictions Genetics family study of twins and their siblings. Data collection for this work was supported by Academy of Finland grants and NIH grant DA12854 (PAFM), and further support was from ENGAGE–European Network for Genetic and Genomic Epidemiology, FP7-HEALTH-F4-2007, grant agreement 201413; the Academy of Finland Center of Excellence in Complex Disease Genetics (grants 213506 and 129680; to J Kaprio), and the Academy of Finland (grants 141054, 265240, and 263278; to J Kaprio). The US Kidney Cancer Study (USKCS) was supported, in part, by the intramural research program of the Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH. The KORA study was initiated and financed by the Helmholtz Zentrum München– German Research Center for Environmental Health, which is funded by the German Federal Ministry of Education and Research (BMBF) and by the State of Bavaria. Furthermore, KORA research was supported within the Munich Center of Health Sciences (MC-Health), Ludwig-Maximilians-Universität, as part of LMUinnovativ. The Erasmus Rucphen Family (ERF) study was supported by grants from The Netherlands Organisation for Scientific Research (NWO), Erasmus MC, the Center for Medical Systems Biology (CMSB), the European Community's Seventh Framework Programme (FP7/2007-2013), the ENGAGE Consortium (grant HEALTH-F4-2007- 201413), and the Netherlands Consortium for Healthy Ageing (grant 050-060-810). We are grateful to all general practitioners for their contributions, to Petra Veraart for her help in genealogy, Jeannette Vergeer for the supervision of the laboratory work, and Peter Snijders for his help in data collection. The Rotterdam Study is supported by the Erasmus MC and Erasmus University Rotterdam; the Netherlands Organization for Scientific Research (NWO); the Netherlands Organization for Health Research and Development (ZonMw); the Research Institute for Diseases in the Elderly (RIDE); the Netherlands Genomics Initiative (NGI); the Ministry of Education, Culture, and Sciences; the Ministry of Health, Welfare, and Sports; the European Commission (DG XII); and the Municipality of Rotterdam. The analysis of data from the NHANES was funded by grant R01 AG033592 from the National Institute on Aging (PI: Elissa Epel). The Zutphen Elderly Study was supported by grants from The Netherlands Prevention Foundation (Preventiefonds) and the survey on Crete was financially supported by Danone. The RPCI study was supported by grants from Susan G Komen for the Cure (BCTR 0600562) and the US NIH Cancer Center Support Grant (P30 CA016056). The Heart Scan Study was funded by the British Heart Foundation (RG/10/005/28296) and the Medical Research Council, United Kingdom. A Steptoe is funded by the British Heart Foundation. The Helsinki Businessmen Study (HBS) was initially funded by the Academy of Finland and various foundations during the decades, most recently by the Sohlberg Foundation, the Jahnsson Foundation, and Gustav Voch Victoria Frimurarestiftelse. Funding has also been provided by the Helsinki University Central Hospital, the Oulu University Hospital (VTR/EVO-funding), and the Academy of Finland grants 286294 and 294154. Laboratory analysis of the Cebu Longitudinal Health and Nutrition Survey (CLHNS) was funded by NSF (Doctoral Dissertation Improvement grant BCS-0962282), the Wenner-Gren Foundation (grant 8111), and institutional support from Northwestern University; data and sample collection were funded by the NIH (grants RR20649 and ES10126). DNA extracts were provided by Karen Mohlke. Richard Cawthon, Justin Tackney, Katarina Nordfjäll, Klelia Salpea, Christine Ackerman, and Margrit Urbanek provided laboratory advice. We especially thank the many researchers at the Office of Population Studies, University of San Carlos, Cebu, the Philippines, for their central role in study design and data collection, and the Filipino participants who provided their time and samples for this study. NESDA is funded through the Geestkracht program of the Netherlands Organization for Health Research and Development (Zon-Mw, grant 10-000-1002) and is supported by participating universities and mental health care organizations. JE Verhoeven and telomere length assaying were supported through an NWO-VICI grant (91811602). The authors of PRT thank Gislaine Satyko Kogure and Rafael Costa Silva for personal guidance regarding progressive resistance training. We are grateful to the São Paulo Research Foundation (Fundação de Amparo à Pesquisa do Estado de São Paulo–FAPESP), for financial support with grants 2010/08800-8 and 2012/11566-2 (RMR) and fellowship 2012/11069-9 (CLMF). Fadi Charchar's work was supported by a National Health and Medical Research Council of Australia grant APP1123472. Maciej Tomaszewski's work on the Y chromosome was supported by British Heart Foundation project grants PG/16/49/32176 and PG/12/9/29376. The ESTHER study was funded by the Baden-Württemberg State Ministry of Science, Research, and Arts (Stuttgart, Germany); the Federal Ministry of Education and Research (Berlin, Germany); and the Federal Ministry of Family Affairs, Senior Citizens, Women, and Youth (Berlin, Germany). The Berlin Aging Study II (BASE-II) was supported by the German Federal Ministry of Education and Research (BMBF grants 16SV5536K, 16SV5537, 16SV5538, and 16SV5837; previously 01UW0808)). The study “Sex- and Gender-Sensitive Prevention of Cardiovascular and Metabolic Disease in Older Adults in Germany” (GendAge) includes analyses of BASE-II baseline data and follow-up analyses of BASE-II participants with a focus on sex and gender differences in cardiovascular and metabolic diseases. GendAge is also supported by the BMBF (grants 01GL1716A and 01GL1716B). Additional contributions (e.g., financial, equipment, logistics, personnel) were made from each of the other participating sites [i.e., the Max Planck Institute for Human Development (MPIB); Max Planck Institute for Molecular Genetics (MPIMG); Charite-Universiätsmedizin, University Medicine, German; Institute for Economic Research (DIW) Humboldt-Universität zu Berlin; all located in Berlin, Germany, and University of Lübeck in Lübeck, and University of Tübingen, Germany].

This study corresponds to a substudy of “Venado Tuerto 2.” The authors thank Hugo Grancelli, Dra Monica Damiano, Daniel Fox, and Carlos Ranalli; the Regional Centre for Development; School of Social Work of Venado Tuerto; Nutrition Society of Venado Tuerto; and the Department of Cardiology Hospital Alejandro Gutierrez of Venado Tuerto for their collaboration.

GAHR2 was supported by grants awarded to Dr. D'Antono by the Canadian Institutes of Health Research (CIHR; MOP 79456 and 111017) and the Montreal Heart Institute Foundation. The RPE study was funded by research grants from the Singapore Ministry of Education, the AXA Research Foundation, and the Templeton World Charity Foundation, Inc. The Sweden Mindfulness Study was supported by the Swedish Research Council and FORTE to Kristina Sundquist and Jan Sundquist and the National Heart, Lung, and Blood Institute of the NIH under award R01HL116381 to Kristina Sundquist. The Ukrainian part of the data of the Kyev study was completed within the framework of the “Research of anthropometric, biochemical and genetic risk factors of development of the glucose regulation disturbances and diabetes mellitus in Ukraine study” conducted at the Komisarenko Institute of Endocrinology and Metabolism (National Academy of Medical Sciences (grant 0116 U 002166).

The authors’ responsibilities were as follows—MG: wrote the manuscript, researched data, contributed to the design and discussion, and had responsibility for final content; GJH, EEA, and MPZ: contributed to the design and discussion and reviewed and edited the manuscript; KN, MM, MB, TdM, AEH, EJG, SCH, JAN, KDS, VAD, RF-F, GA, SEH, LH, DG, IH, JDK, HN, DJK, KAM, PW, Y-LZ, SP, EWD, LR, DB, AS, LB Boardman, AM, BN, WZ, RR-G, AJP, JK, JNH, CG, G Paolisso, JBHH, LM, TS, JW, KS, PvdH, LB Broer, FK, BK, TS, DTAE, CD, JEV, RS, RZ, RMRdR, FJC, MTT, UM, ID, AEIM, GC, DK, BD, MK, BJM, RPE, KS, G Pare, and MC: researched data and reviewed and edited the manuscript; and all authors: read and approved the final manuscript. None of the authors declared a conflict of interest.

Notes

The authors reported no funding received for this study.

Supplemental Methods, Supplemental Results, Supplemental Figures 1 and 2, Supplemental Tables 1–3, and Study Protocol for Participating PIs are available from the “Supplementary data” link in the online posting of the article and from the same link in the online table of contents at https://academic.oup.com/ajcn/.

Abbreviations used: bp, base pair; PI, principal investigator; qPCR, quantitative polymerase chain reaction; TL, telomere length; T/S ratio, telomere to single-copy gene ratio.

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