There are limited data on the association between dietary information and leukocyte telomere length (LTL), which is considered an indicator of biological aging. In this study, we aimed at determining the association between dietary patterns or consumption of specific foods and LTL in Korean adults.
A total of 1958 middle-aged and older Korean adults from a population-based cohort were included in the study. Dietary data were collected from a semi-quantitative food frequency questionnaire at baseline (June 2001 to January 2003). LTL was assessed using real-time PCR during the 10-year follow-up period (February 2011 to November 2012).
We identified two major factors and generated factor scores using factor analysis. The first factor labeled ‘prudent dietary pattern’ was characterized by high intake of whole grains, seafood, legumes, vegetables and seaweed, whereas the second factor labeled ‘Western dietary pattern’ was characterized by high intake of refined grain, red meat or processed meat and sweetened carbonated beverages. In a multiple linear regression model adjusted for age, sex, body mass index and other potential confounding variables, the prudent dietary pattern was positively associated with LTL. In the analysis of particular food items, higher consumption of legumes, nuts, seaweed, fruits and dairy products and lower consumption of red meat or processed meat and sweetened carbonated beverages were associated with longer LTL.
Our findings suggest that diet in the remote past, that is, 10 years earlier, may affect the degree of biological aging in middle-aged and older adults.
Telomere length shortens in cellular aging, representing an indicator of biological aging. Telomeres, which consist of repetitive DNA sequences of 5′-TTAGGG-3′, protect the ends of human chromosomes from oxidative damage and shorten gradually with every cell division because of the ‘end-replication problem’.1, 2, 3 The rate of telomere shortening is not constant and differs between people.4 Recent studies have reported that telomere length is associated with oxidative and inflammatory stress and clinical outcomes such as diabetes mellitus,5, 6 hypertension,6 coronary heart disease,7 dementia8 and mortality.9 Telomere length is also influenced by multiple factors including genetics, socioeconomic status, obesity, diet, cigarette smoking, sedentary activity and environmental pollution.10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22 Some studies have reported an association between diets15, 18 or consumption of specific foods15, 20, 21, 22 and telomere length. One observational study found a positive association between telomere length and the Mediterranean diet score, which was formulated on the basis of the Mediterranean dietary pattern that is characterized by high intake of whole grains, legumes, vegetables, fruits, seafood, dairy products, nuts and olive oil.18 However, another observational study found no association between telomere length and dietary patterns, which were empirically derived from dietary data.15 Thus, additional data are warranted on dietary patterns and their association with telomere length. Studies focusing on specific foods showed that a higher consumption of tea,20 vegetables and fruits21, 22 is associated with longer telomere length, whereas consumption of processed meat is inversely associated with telomere length.15
In this study, we explored major dietary patterns and analyzed the association between dietary patterns and leukocyte telomere length (LTL). In addition, we examined a broad range of food items or foods groups, which were used to derive dietary patterns, and examined their association with LTL.
Subjects and methods
Study population and study design
The study participants were from a population-based cohort from the Korean Genome Epidemiology Study, an ongoing prospective study. Detailed information on the study design and procedures of the study is available.23, 24 Briefly, cohort members are residents of Ansan City, Republic of Korea, and were 40–69 years of age during the baseline period (June 2001 to January 2003). A total of 5012 participants completed the baseline health examination and questionnaire-based interview at the Korea University Ansan Hospital. During the health examination, trained health professionals measured the blood pressure and anthropometric parameters of the participants and collected bio-specimens for biochemical assays. The interview included discussion of demographic and medical information, health condition, family history of particular diseases, lifestyle and dietary intake. A standardized health examination and an interview were then performed biennially. At each visit, the participants signed an informed consent form approved by the Human Subjects Review Committee of Korea University Ansan Hospital (ED0624).
We used the baseline data on demographic and medical information, lifestyle and diet, and analyzed the LTL from blood samples collected during the follow-up period (February 2011 to November 2012). In a cross-sectional study design, dietary information collected in the past, that is, 10 years earlier, was used as exposure to imply a potential temporal relationship with LTL outcomes, although the earlier habitual diet was likely to reflect the diet 10 years later.
A total of 2314 participants had complete data, and the following subjects were excluded: those considered to have outlying LTL values (LTL >4; n=2), those diagnosed with cancer or cardiovascular disease (n=277), those without complete information on confounding variables (n=55) or those with inappropriate calorie intake (<500 or ⩾5000 kcal per day; n=22). Finally, data of 1958 participants were entered into the analysis.
Information on dietary intake was collected using the semi-quantitative food frequency questionnaire (FFQ), which was developed and validated by the Korea Centers for Disease Control and Prevention (Seoul, Korea).25 The FFQ collects information on the average consumption frequency and serving size for 103 food items and beverages consumed in the previous year; there are nine categories of consumption frequency (‘almost never’, ‘once a month’, ‘2–3 times a month’, ‘1-2 times a week’, ‘3-4 times a week’, ‘5-6 times a week’, ‘once a day’, ‘twice a day’ or ‘3 times a day’) and three categories of serving size (‘larger than’, ‘equal to’ or ‘smaller than’ a standard serving size). During the interview using the FFQ, trained interviewers showed pictures of foods to the participants to help them estimate the serving size. To calculate the daily average consumption frequency of each food item, the frequency was multiplied by 1.5 for larger amounts, one for an equal amount and 0.5 for smaller amounts as compared with the standard serving size.
Measurement of LTL
Relative LTL was measured using quantitative real-time PCR.26 Genomic DNA in leukocytes was extracted from peripheral blood samples using the QIAamp DNA blood mini kit (Qiagen, Hilden, Germany); these blood samples were collected during the 10-year follow-up period. Purified DNA samples were diluted and quantified using a NanoDrop 1000 spectrophotometer (Thermo Fisher Scientific, Wilmington, DE, USA). The ratio of the telomere repeat copy number to the single-copy gene (36B4 gene, which encodes acidic ribosomal phosphoprotein) copy number, was determined for relative LTL by using the iQ Multi-Color Real-Time PCR Detection System (Bio-Rad, Hercules, CA, USA). The final concentrations of the PCR reagents were 1 × SYBR Green SuperMix (Bio-Rad), 50 ng of DNA, 0.2 μm of telomere primers (forward, 5′-IndexTermGGTTTTTGAGGGTGAGGGTGAGGGTGAGGGTGAGGGT-3′; reverse, 5′-IndexTermTCCCGACTATCCCTATCCCTATCCCTATCCCTATCCCTA-3′) and 0.3 μm of 36B4 primers (forward, 5′-IndexTermCAGCAAGTGGGAAGGTGTAATCC-3′; reverse, 5′-IndexTermCCCATTCTATCATCAACGGGTACAA-3′). The reactions were performed using a telomere and 36B4 primers in the same 96-well plate, and each plate included a reference DNA sample. A four-point standard curve was established to transform the cycle threshold into nanograms of DNA. A validity test showed that the Pearson‘s correlation coefficients were 0.78 for the intra-assay and 0.69 for the inter-assay when 25 samples were run in triplicate.
Potential confounding variables
Information on potential confounding variables including age, sex, income status (family’s monthly income), body mass index, smoking status, alcohol consumption status, physical activity, calorie intake, and the presence of diabetes, hypertension or hypercholesterolemia was collected from the baseline data from the health examination and questionnaire-based interview. Body weight and height were measured to the nearest 0.1 kg and 0.1 cm, respectively, and body mass index (kg m−2) was calculated. For physical activity, metabolic equivalent of scores were calculated as reported in a previous study.23 Average daily calorie intake was calculated using the FFQ data and the food composition data published by the Rural Development Administration of Korea.27 The presence of diabetes mellitus, hypertension or hypercholesterolemia was confirmed if one of the following criteria was met: use of hypoglycemic medications or fasting plasma glucose level ⩾120 mg or post-prandial glucose level ⩾200 mg for diabetes mellitus, use of antihypertensive medications or systolic blood pressure ⩾140 mm Hg or diastolic blood pressure ⩾90 mm Hg for hypertension and use of hypolipidemic medications or serum total cholesterol level ⩾240 mg for hypercholesterolemia. Blood pressure was measured using a mercury sphygmomanometer with each subject in a sitting position and was recorded to the nearest 2 mm Hg. The average of two measurements in the left and right arms was calculated.
Descriptive statistics, the χ2-test, Student’s t-test and analysis of variance were used to analyze data where appropriate. On the basis of a previous study,24 103 food items were re-grouped into 27 food items. Consumption frequencies of the food items were calculated and used for factor analysis. To determine the number of major factors, which were labeled as dietary patterns in this study, a scree plot, which is a two-dimensional graph with eigenvalues on the y axis and the number of factors on the x axis, was created; the slope of the plot curve changed abruptly until the number of factors was three. Thus, two factors were determined as the optimal number of major factors and were rotated by an orthogonal transformation (Varimax rotation method) to improve the interpretability of factor loadings, which are correlations between the variable and the factor. To characterize dietary patterns, variables with a factor loading ⩾0.4, which is conventionally used as a cutoff point in the interpretation.28 Factor scores for each factor were generated for assignment to each subject. To test the associations of dietary patterns and consumption of food items with LTL, multiple linear regression models were constructed. In the models, LTL was natural logarithm-transformed to be fitted as a dependent variable and following potential confounding variables were adjusted for: age (continuous variable), sex, income status (five categories: <106 won, 106 to 1.9 × 106 won, 2 × 106 to 2.9 × 106 won, 3 × 106 to 3.9 × 106 won or ⩾4 × 106 won; 106 won is equal to 940 USD according to the exchange rate reported on 15 October 2014), body mass index (three categories:<23 kg m−2, 23–24.9 kg m−2, or ⩾25 kg m−2 based on the body mass index criteria for Asia and Oceania),29 smoking status (4 categories: non-smoking, smoking of⩽10 cigarettes/day, smoking of 10.1–20 cigarettes/day, or smoking of >20 cigarettes/day), alcohol consumption status (2 categories: abstainer or drinker), physical activity (a continuous variable), calorie intake (a continuous variable), and the presence of diabetes, hypertension, or hypercholesterolemia (binary variables) were adjusted for. Furthermore, multiple regression analysis was stratified by sex. All testing was based on a two-sided level of significance. The SAS program (version 9.3, 2013, SAS Institute, Cary, NC, USA) was used to conduct statistical analyses. In particular, SAS procedures such as PROC FACTOR, PROC SCORE, and PROC REG were used for analyses.
The baseline characteristics of the study participants were compared across categories (Table 1). Participants who were non-smokers or non-drinkers or who had lower monthly income or lower level of physical activity were likely to have longer LTL. When we compared the same characteristics examined during the 10-year follow-up visit, the distributions across categories were similar to those for the baseline characteristics except for income, smoking status and alcohol consumption: more people who had a higher monthly income, were non-smokers, or were non-drinkers after 10 years, and the statistical significance of these variables disappeared (data available from the authors on request).
Table 2 shows the factor loadings after varimax rotation. On the basis of major food items, the first factor was labeled ‘prudent dietary pattern’ and the second was labeled ‘Western dietary pattern’. The prudent dietary pattern was characterized by the high intake of whole grains, fish and seafood, legumes, vegetables and seaweed whereas the Western dietary pattern was characterized by the high intake of refined grain, red meat or processed meat and sweetened carbonated beverages.
Table 3 shows the association between the factor scores of each dietary pattern and log-transformed LTL. After accounting for confounding variables, the prudent dietary pattern was positively associated with LTL. There was an inverse trend between the Western dietary pattern and LTL, but no significance was observed (Table 3).
Table 4 shows results for the associations between individual food items, which contributed to major dietary patterns, and log-transformed LTL. In multiple models, LTL was positively associated with the consumption of legumes, nuts, seaweed, fruits, dairy products and coffee. In contrast, it was inversely associated with consumption of red meat or processed meat and sweetened carbonated beverages. Even after all food items were fitted in multiple models, the significance did not change (data available from the authors on request). In further analyses, data stratified by sex showed that associations for consumption of red meat or processed meat, seaweed or fruits were stronger among men, whereas an association for coffee consumption was stronger among women (data available from the authors on request).
In this longitudinal study, we identified the prudent dietary pattern, which was characterized by a high consumption of whole grains, fish and seafood, legumes, vegetables and seaweed, from dietary information in the remote past, that is, 10 years earlier, and found that this dietary pattern is positively associated with longer LTL. In analyses of individual food items, higher consumption of legumes, nuts, seaweed, fruits and dairy products was associated with longer LTL. In contrast, higher consumption of red meat or processed meat and sweetened carbonated beverages, which were included in the Western dietary pattern, was associated with shorter LTL. Although we used data-driven approach with a certain amount of subjective judgment in determining dietary patterns and showed a weak association between dietary patterns and LTL, we could add supportive data on the potential role of diet in biological aging.
Telomeres are long repetitive 5′-TTAGGG-3′ sequences in humans and protect the ends of eukaryotic chromosomes from deterioration or chromosomal fusion. However, they shorten after every cell division because of the ‘end-replication problem’. In the lagging strand replication, RNA primers on the Okazaki fragments are replaced by DNA synthesis, but the terminal RNA primer at the end of the strand is eliminated as a result of telomere loss.1, 2 Thus, the telomere length shortens with cellular aging, representing a ‘biological clock’,3 and is influenced by the balance between oxidative stress and anti-oxidative response.4, 5, 30
It is hypothesized that diets or foods rich in anti-oxidant nutrients reduce the burden of oxidative stress; importantly, they may also delay the shortening rate of telomere length. Data are still limited on diets associated with telomere length. One study used data-driven dietary patterns, which include two major patterns such as a diet rich in fats and processed meats and a diet of whole grains and fruit, but found no association of the diets with telomere length.15 Another study used the Mediterranean diet score for a Mediterranean dietary pattern and observed a significant association between the diet score and telomere length in Caucasians.18 Thus far, there have been no reports on dietary patterns associated with telomere length in Asian populations. A few studies have examined individual food items or food groups such as tea,20 vegetables and fruits21, 22 and red meat15 in association with telomere length.
In the present study, we examined the beneficial effects of a prudent dietary pattern on telomere length. Considering a broad range of food items, we found that higher consumption of legumes, nuts, seaweed, fruits and dairy products is associated with longer telomere length, whereas consumption of red meat or processed meat and sweetened carbonated beverages is associated with short telomere length. In terms of the practical application of our data, the regression coefficient of LTL per 10-year reduction in chronological age is comparable to that of daily consumption of one serving of legumes or a half serving of seaweed.
Although the potential biological mechanisms underlying the associations of these food items with telomere length need to be clarified in future studies, it is possible that the associations are based on the potential roles of nutrient components in telomeric biology. Legumes contain a variety of phytochemicals such as isoflavones that have anti-oxidant properties as well as folic acid, which may have an important role in DNA methylation and integrity.31, 32 Nuts are major food sources of polyunsaturated fatty acids, particularly omega-3 fatty acids, and vitamin E.33, 34 Further, seaweed contains anti-oxidative components.35 Fruits are also widely known to have nutrients with anti-oxidant properties, for example, anthocyanin, which is present abundantly in raspberries and strawberries.36 A recent study demonstrated a positive association between the levels of fruit intake and telomere length.37 Consumption of dairy products may be indirectly associated with telomere shortening caused by diabetes mellitus, which tends to induce oxidative stress.38 It is, however, unclear which specific constituents of dairy products are linked to telomere length. In contrast to dairy products, consumption of red meat and processed products is likely to increase oxidative stress; previous studies have shown an inverse association between consumption of red meat/processed products and telomere length.15, 39 It has been reported that high consumption of sweetened carbonated beverages is associated with diseases related to oxidative stress, such as metabolic syndrome, diabetes mellitus and cardiovascular disease.40
The findings of our study are important mainly because we used data from a population-based cohort and considered a broad range of food items. However, there were a few limitations that need to be acknowledged when interpreting these findings. We assessed LTL, which is shorter compared with telomere length measured using somatic cells because the half-life of leukocytes is shorter. However, it was recently reported that the rates of telomere shortening are similar in leukocytes and somatic tissues.41 Thus, our results of the associations with LTL may be generalized to telomere length in somatic tissues. Although the FFQ that we used provides valid dietary information over a long period, generally, this dietary assessment method may produce less accurate data than multiple dietary records. In addition, generalizability of the findings may be limited; in particular the findings on dietary patterns may not be reproducible in other populations. In future studies, thus, the association between dietary patterns or specific foods mainly consumed and telomere length should be investigated in other racial and ethnic groups.
In conclusion, our investigation showed that a diet in the remote past, that is, 10 years earlier, is associated with LTL, reflecting biological aging, among middle-aged and older adults. Further studies are warranted to determine a causal relationship between dietary patterns or food items and telomere shortening.
Levy MZ, Allsopp RC, Futcher AB, Greider CW, Harley CB . Telomere end-replication problem and cell aging. J Mol Biol 1992; 225: 951–960.
Harley CB, Futcher AB, Greider CW . Telomeres shorten during ageing of human fibroblasts. Nature 1990; 345: 458–460.
Zhu H, Belcher M, van der Harst P . Healthy aging and disease: role for telomere biology? Clin Sci (Lond) 2011; 120: 427–440.
Von Zglinicki T . Oxidative stress shortens telomeres. Trends Biochem Sci 2002; 27: 339–344.
Aviv A, Valdes A, Gardner JP, Swaminathan R, Kimura M, Spector TD et al. Menopause modifies the association of leukocyte telomere length with insulin resistance and inflammation. J Clin Endocrinol Metab 2006; 91: 635–640.
Demissie S, Levy D, Benjamin EJ, Cupples LA, Gardner JP, Herbert A et al. Insulin resistance, oxidative stress, hypertension, and leukocyte telomere length in men from the Framingham Heart Study. Aging Cell 2006; 5: 325–330.
Spyridopoulos I, Hoffmann J, Aicher A, Brümmendorf TH, Doerr HW, Zeiher AM et al. Accelerated telomere shortening in leukocyte subpopulations of patients with coronary heart disease: role of cytomegalovirus seropositivity. Circulation 2009; 120: 1364–1372.
von Zglinicki T, Serra V, Lorenz M, Saretzki G, Lenzen-Grossimlighaus R, Gessner R et al. Short telomeres in patients with vascular dementia: an indicator of low antioxidative capacity and a possible risk factor? Lab Invest 2000; 80: 1739–1747.
Cawthon RM, Smith KR, Obrien E, Sivatchenko A, Kerber RA . Association between telomere length in blood and mortality in people aged 60 years or older. Lancet 2003; 361: 393–395.
Valdes AM, Andrew T, Gardner JP, Kimura M, Oelsner E, Cherkas LF et al. Obesity, cigarette smoking, and telomere length in women. Lancet 2005; 366: 662–664.
Jazwinski SM . Longevity, genes, and aging. Science 1996; 273: 54–59.
Slagboom PE, Droog S, Boomsma DI . Genetic determination of telomere size in humans: a twin study of three age groups. Am J Hum Genet 1994; 55: 876–882.
Jeanclos E, Schork NJ, Kyvik KO, Kimura M, Skurnick JH, Aviv A et al. Telomere length inversely correlates with pulse pressure and is highly familial. Hypertension 2000; 36: 195–200.
Kiecolt-Glaser JK, Epel ES, Belury MA, Andridge R, Lin J, Glaser R et al. Omega-3 fatty acids, oxidative stress, and leukocyte telomere length: a randomized controlled trial. Brain Behav Immun 2012; 28: 16–24.
Nettleton JA, Diez-Roux A, Jenny NS, Fitzpatrick AL, Jacobs DR . Dietary patterns, food groups, and telomere length in the Multi-Ethnic Study of Atherosclerosis (MESA). Am J Clin Nutr 2008; 88: 1405–1412.
Cherkas LF, Hunkin JL, Kato BS, Richards JB, Gardner JP, Surdulescu GL et al. The association between physical activity in leisure time and leukocyte telomere length. Arch Intern 2008; 168: 154–158.
Hoxha M, Dioni L, Bonzini M, Pesatori AC, Fustinoni S, Cavallo D et al. Association between leukocyte telomere shortening and exposure to traffic pollution: a cross-sectional study on traffic officers and indoor office workers. Environ Health 2009; 8: 41.
Boccardi V, Esposito A, Rizzo MR, Marfella R, Barbieri M, Paolisso G et al. Mediterranean diet, telomere maintenance and health status among elderly. PLoS One 2013; 8: e62781.
García-Calzón S, Gea A, Razquin C, Corella D, Lamuela-Raventós RM, Martínez JA et al. Longitudinal association of telomere length and obesity indices in an intervention study with a Mediterranean diet: the PREDIMED-NAVARRA trial. Int J Obes (Lond) 2014; 38: 177–182.
Chan R, Woo J, Suen E, Leung J, Tang N . Chinese tea consumption is associated with longer telomere length in elderly Chinese men. Br J Nutr 2010; 103: 107–113.
Marcon F, Siniscalchi E, Crebelli R, Saieva C, Sera F, Fortini P et al. Diet-related telomere shortening and chromosome stability. Mutagenesis 2012; 27: 49–57.
Tiainen AM, Männistö S, Blomstedt PA, Moltchanova E, Perälä MM, Kaartinen NE et al. Leukocyte telomere length and its relation to food and nutrient intake in an elderly population. Eur J Clin Nutr 2012; 66: 1290–1294.
Baik I, Shin C . Prospective study of alcohol consumption and metabolic syndrome. Am J Clin Nutr 2008; 87: 1455–1463.
Baik I, Lee M, Jun NR, Lee JY, Shin C . A healthy dietary pattern consisting of a variety of food choices is inversely associated with the development of metabolic syndrome. Nutr Res Pract 2013; 7: 233–241.
Ahn Y, Kwon E, Shim JE, Park MK, Joo Y, Kimm K et al. Validation and reproducibility of food frequency questionnaire for Korean genome epidemiologic study. Eur J Clin Nutr 2007; 61: 1435–1441.
Cawthon RM . Telomere measurement by quantitative PCR. Nucleic Acids Res 2002; 30: e47.
National Rural Living Science Institute Food composition table, 5th edn. Rural Development Administration of Korea: Suwon, Korea, 1996.
Stevens JP . Applied multivariate statistics for the social sciences 2nd edn, Erlbaum: Hillsdale, NJ, USA, 1992.
Stommel M, Schoenborn CA . Variations in BMI and prevalence of health risks in diverse racial and ethnic populations. Obesity (Silver Spring) 2010; 18: 1821–1816.
Saretzki G, Von Zglinicki T . Replicative aging, telomeres, and oxidative stress. Ann NY Acad Sci 2002; 959: 24–29.
Meyer KA, Kushi LH, Jacobs DR Jr, Slavin J, Sellers TA, Folsom AR et al. Carbohydrates, dietary fiber, and incident type 2 diabetes in older women. Am J Clin Nutr 2000; 71: 921–930.
Anderson JW, Smith BM, Washnock CS . Cardiovascular and renal benefits of dry bean and soybean intake. Am J Clin Nutr 1999; 70: 464S–474S.
Fan C, Zirpoli H, Qi K . n-3 fatty acids modulate adipose tissue inflammation and oxidative stress. Curr Opin Clin Nutr Metab Care 2013; 16: 124–132.
Denis I, Potier B, Vancassel S, Heberden C, Lavialle M . Omega-3 fatty acids and brain resistance to ageing and stress: body of evidence and possible mechanisms. Ageing Res Rev 2013; 12: 579–594.
Heo SJ, Park EJ, Lee KW, Jeon YJ . Antioxidant activities of enzymatic extracts from brown seaweeds. Bioresour Technol 2005; 96: 1613–1623.
Proteggente AR, Pannala AS, Paganga G, Van Buren L, Wagner E, Wiseman S et al. The antioxidant activity of regularly consumed fruit and vegetables reflects their phenolic and vitamin C composition. Free Radic Res 2002; 36: 217–235.
Hou L, Savage SA, Blaser MJ, Perez-Perez G, Hoxha M, Dioni L et al. Telomere length in peripheral leukocyte DNA and gastric cancer risk. Cancer Epidemiol Biomarkers Prev 2009; 18: 3103–3109.
Silva Ton T, Gonçalves Alfenas RD . Dairy products consumption versus type 2 diabetes prevention and treatment; a review of recent findings from human studies. Nutr Hosp 2013; 28: 1384–1395.
O'Callaghan NJ, Toden S, Bird AR, Topping DL, Fenech M, Conlon MA et al. Colonocyte telomere shortening is greater with dietary red meat than white meat and is attenuated by resistant starch. Clin Nutr 2012; 31: 60–64.
Malik VS, Popkin BM, Bray GA, Després JP, Hu FB . Sugar-sweetened beverages, obesity, type 2 diabetes mellitus, and cardiovascular disease risk. Circulation 2010; 121: 1356–1364.
Daniali L, Benetos A, Susser E, Kark JD, Labat C, Kimura M et al. Telomeres shorten at equivalent rates in somatic tissues of adults. Nat Commun 2013; 4: 1597.
This study was supported by a fund (2001-347-6111-221, 2002-347-6111-221, 2011-E71004-00 and 2012- E71005-00) by research of Korea Centers for Disease Control and Prevention and by National Research Foundation of Korea Grant funded by the Korean Government (NRF-2014R1A2A2A01004863). The funders have no role in the study.
The authors declare no conflict of interest.
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Lee, J., Jun, N., Yoon, D. et al. Association between dietary patterns in the remote past and telomere length. Eur J Clin Nutr 69, 1048–1052 (2015). https://doi.org/10.1038/ejcn.2015.58
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