Abstract
Background: Dietary and genetic factors involved in the one-carbon metabolism pathway may affect telomere length through DNA methylation and synthesis, but this has not been comprehensively investigated in epidemiologic studies.
Objective: We cross-sectionally examined associations between dietary and genetic factors in the one-carbon metabolism pathway and relative peripheral blood leukocyte telomere length.
Design: A total of 1715 participants from the Nurses’ Health Study (NHS) had measurements of relative telomere length and plasma concentrations of folate, vitamin B-6, vitamin B-12, cysteine, and homocysteine. Food-frequency questionnaire (FFQ) measurements were also used for the assessment of folate, choline, methionine, riboflavin, vitamin B-6, vitamin B-12, and alcohol intakes. Genotyping was performed on 475 participants with telomere measurements on 29 mostly nonsynonymous single-nucleotide polymorphisms (SNPs) involved in one-carbon metabolism. Unconditional logistic and linear regression models were used.
Results: There were no significant dose-response relations between any plasma- or FFQ-measured dietary factors and relative telomere length in multivariate analyses. For folate, vitamin B-6, and vitamin B-12, results from the use of FFQ data were consistent with plasma-biomarker findings. We showed no significant associations that involved SNPs and relative telomere length after we accounted for the false discovery rate.
Conclusion: Our analyses involving plasma and questionnaire measurements of one-carbon metabolism factors show that some key dietary and genetic factors in this metabolic network are not associated with relative peripheral blood leukocyte telomere length.
INTRODUCTION
Dietary and genetic factors in the one-carbon metabolism pathway may affect telomere length through DNA methylation and synthesis, but few epidemiologic studies have investigated these associations. Several review articles have outlined various biological mechanisms by which one-carbon metabolism factors can affect telomere length (1–3). However, to our knowledge, there have been only 2 epidemiologic studies of one-carbon metabolism and telomere length, and they examined only plasma folate, homocysteine, and vitamin B-12 (4, 5).
The transfer of single-carbon groups in the one-carbon metabolism network is essential for DNA methylation and synthesis (6). Folate is an important methyl group donor that allows the synthesis of methionine from homocysteine, which is a process that requires the vitamin B-12 cofactor. Then, the converted form of methionine, S-adenosyl methionine, can methylate DNA. Besides its role in DNA methylation, folate is also required for nucleotide synthesis, including deoxythymidine monophosphate (dTMP)5 and purine synthesis. Other nutrients such as choline, riboflavin, and vitamin B-6 also influence this metabolic network. Choline is a source of methyl groups, whereas riboflavin and the biologically active form of vitamin B-6 pyridoxal 50-phosphate (PLP), are important cofactors. Alcohol intake is known to reduce folate absorption and bioavailability.
Telomeres are repetitive DNA sequences that protect the ends of linear chromosomes (7). Telomeres shorten over time in somatic cells because DNA polymerases are not able to fully replicate chromosomes during cell division, which is also known as the end-replication problem (8). Somatic cells lack telomerase activity to restore telomere length, whereas in embryonic stem cells, telomerase restores telomere length by adding hexameric repeats to chromosome ends (9). Because one-carbon metabolism is crucial in the maintenance of DNA integrity, telomere length may be influenced by dietary and genetic factors involved in this metabolic network.
This cross-sectional analysis of Nurses’ Health Study (NHS) data comprehensively examines associations between dietary and genetic factors in one-carbon metabolism and relative telomere length in peripheral blood leukocytes, by using plasma and food-frequency questionnaire (FFQ) measurements. Dietary factors include plasma concentrations of folate, PLP/vitamin B-6, vitamin B-12, cysteine, and homocysteine as well as FFQ measurements of folate, choline, methionine, riboflavin, vitamin B-6, vitamin B-12, and alcohol intakes. Genetic factors include 29 potentially important and mostly nonsynonymous single-nucleotide polymorphisms (SNPs) in the one-carbon metabolism pathway.
SUBJECTS AND METHODS
Study population
The NHS is a prospective cohort study that began in 1976 when 121,700 female registered nurses aged 30–55 who were residing in 11 US states completed an initial questionnaire. Personal information, such as lifestyle and dietary factors, was subsequently updated every 2 or 4 y through questionnaire responses. From 1989 to 1990, blood was collected from 32,826 participants; 97% of these blood samples arrived within 26 h of being drawn and were centrifuged and divided into plasma, white blood cell, and red blood cell components. Cryotubes that contained these components were stored in liquid-nitrogen freezers. This analysis was restricted to whites, who make up the predominant majority of NHS participants. A total of 1715 participants from previous breast cancer case-control sets were measured for one-carbon metabolism biomarker concentrations and leukocyte telomere length (10–12). Genotyping was performed on 475 participants with a telomere-length measurement from an endometrial cancer case-control set (13). The NHS protocol was approved by Brigham and Women's Hospital's Human Research Committee.
One-carbon metabolism biomarkers
Plasma folate and vitamin B-12 were measured by using a radioassay (Bio-Rad). Plasma PLP/vitamin B-6 concentrations were measured by using an enzymatic procedure with radioactive tyrosine and tyrosine decarboxylase. Plasma homocysteine and cysteine were measured by using HPLC with fluorescence detection (14). No significant differences in mean concentrations of plasma biomarkers were detected by breast cancer case-control status in the NHS. The mean CV was 6.5% for folate, 7.9% for homocysteine, 7.2% for PLP/vitamin B-6, 7.3% for vitamin B-12 (11), and 6.8% for cysteine (12).
Telomere length
The relative telomere length in genomic DNA extracted from peripheral blood leukocytes was measured by using quantitative real-time polymerase chain reaction, and the ratio of the telomere repeat copy number to a single gene copy number (T:S) was determined as previously described (15). Each sample was analyzed in triplicate, and the relative telomere length was the exponentiated T:S corrected for a reference sample. CVs for the telomere assay and the single-gene assay were in the ranges of 0.87–1.03% and 0.56–1.09%, respectively. CVs for the exponentiated T:S of quality-control samples were in the range of 14–16.3%.
Questionnaire information
Questionnaire information for potential confounders was obtained from the 1990 follow-up cycle, except for the physical activity level, which was obtained in 1988. The amount of smoking was indicated by pack-years, whereas the physical activity level was indicated by metabolic equivalent tasks per week. Intakes of one-carbon metabolism dietary factors were calculated by using the FFQ information as well as data from the USDA (16, 17) and other sources (18). Multivariate analyses of the association between FFQ-measured dietary intakes and telomere length used the average of total energy intake–adjusted dietary measurements from 1980, 1984, 1986, and 1990 questionnaires.
SNP selection and genotyping
SNPs were selected because they are nonsynonymous ones that are likely to affect protein functionality in one-carbon metabolism (19) or are involved in choline metabolism (20). The genes that carry the SNPs include ALDH1L1, ATIC, BHMT, CHDH, CTH, DNMT1, FOLH1, GART, GGH, MTHFD1, MTHFR, MTR, MTRR, PEMT, SHMT1, and SLC19A1. The minor allele frequencies range from 0.04 to 0.47. Genomic DNA was extracted from blood samples by using the QIAmp 96-spin blood protocol (Qiagen). Genotyping was performed at the Dana Farber/Harvard Cancer Center High-Throughput Genotyping Core by using the 5′ nuclease assay (Taqman; Applied Biosystems). Laboratory personnel were blinded to case-control status, and 5% blinded quality-control samples were inserted to validate genotyping procedures; the concordance for blinded samples was 100%. The amount of missing genotyping data was <4%.
Statistical analyses
After the exclusion of extreme 1% outlier values of relative telomere length, z scores were derived to standardize the distribution. Unconditional logistic regression was used to obtain ORs when the z score outcome was dichotomized at the median. Linear regression was used for tests of trend when the z score was continuous. Potential confounders that were adjusted include age (y; continuous), smoking (0, 0.1–20, 20.1–40, or >40 pack-years), BMI (in kg/m2; <25, 25–29.9, 30–34.9, or ≥35), and physical activity (metabolic equivalent tasks/wk; nominal quartiles). In the multivariate analysis, the complete-case method was used to treat missing information. Adjustment was also made for batches by representing each batch as an indicator variable. Spearman's correlation was used to assess correlations between plasma- and FFQ-measured nutrients. The additive genetic model was used for the SNP analyses, which assumed that the effect of the heterozygous genotype was intermediate between the 2 homozygous genotypes. The homozygous genotype of the reference allele was coded 0. Analyses were done with SAS software (version 9.1; SAS Institute). Quartiles were created by using the rank procedure. All P values were 2 sided.
RESULTS
Characteristics of key variables by quartiles of relative telomere length as well as unadjusted associations between nutritional and lifestyle variables with relative telomere length are shown in Table 1. In this analysis sample, older age (P-trend = 0.0005) and higher plasma cysteine (P-trend = 0.02) were significantly associated with a shorter relative telomere length. At the time of blood draw, the median age of this sample population was 59.8 y and their median BMI was 24.6.
TABLE 1.
Characteristics of key variables by quartiles of relative telomere length1
| Variables | Quartile 1 (n = 491) | Quartile 2 (n = 418) | Quartile 3 (n = 392) | Quartile 4 (n = 414) | P-trend |
| Age (y) | 60.1 ± 6.1 | 59.6 ± 5.9 | 58.9 ± 6.3 | 59.0 ± 6.4 | 0.0005 |
| Smoking (pack-years) | 13.6 ± 20.4 | 11.9 ± 18.0 | 11.7 ± 18.6 | 13.2 ± 19.7 | 0.49 |
| BMI (kg/m2) | 25.6 ± 4.7 | 26.0 ± 5.3 | 25.2 ± 4.3 | 25.3 ± 4.4 | 0.56 |
| Physical activity (MET/wk) | 17.9 ± 33.7 | 16.8 ± 19.5 | 16.7 ± 20.4 | 16.5 ± 18.4 | 0.22 |
| Plasma folate (nmol/L) | 21.8 ± 15.9 | 22.2 ± 15.9 | 24.2 ± 28.8 | 22.2 ± 18.8 | 0.81 |
| Plasma PLP/vitamin B-6 (pmol/mL) | 77.6 ± 89.8 | 74.8 ± 88.2 | 75.3 ± 83.2 | 74.2 ± 95.9 | 0.76 |
| Plasma vitamin B-12 (pmol/L) | 340.5 ± 135.9 | 348.9 ± 216.6 | 357.2 ± 147.5 | 345.8 ± 215.1 | 0.55 |
| Plasma cysteine (nmol/mL) | 303.1 ± 64.1 | 303.4 ± 68.7 | 296.6 ± 65.1 | 294.3 ± 63.4 | 0.02 |
| Plasma homocysteine (nmol/mL) | 12.0 ± 7.8 | 11.6 ± 4.7 | 11.0 ± 3.5 | 11.4 ± 4.3 | 0.17 |
| Dietary FFQ folate (μg/d) | 417.3 ± 183.8 | 416.8 ± 178.0 | 419.8 ± 183.6 | 410.1 ± 178.1 | 0.76 |
| Dietary FFQ choline (mg/d) | 341.8 ± 58.7 | 341.1 ± 54.4 | 338.8 ± 59.7 | 339.0 ± 53.0 | 0.76 |
| Dietary FFQ methionine (g/d) | 1.8 ± 0.3 | 1.8 ± 0.3 | 1.8 ± 0.3 | 1.8 ± 0.3 | 0.81 |
| Dietary FFQ riboflavin (mg/d) | 4.9 ± 7.0 | 4.1 ± 4.9 | 4.4 ± 4.6 | 4.2 ± 5.7 | 0.13 |
| Dietary FFQ vitamin B-6 (mg/d) | 8.6 ± 16.5 | 8.8 ± 20.3 | 8.1 ± 16.6 | 8.7 ± 19.7 | 0.48 |
| Dietary FFQ vitamin B-12 (μg/d) | 10.9 ± 10.0 | 11.0 ± 11.0 | 10.6 ± 7.4 | 11.1 ± 11.0 | 0.20 |
| Dietary FFQ alcohol (g/d) | 6.9 ± 10.0 | 6.2 ± 9.0 | 6.7 ± 10.2 | 7.2 ± 9.6 | 0.88 |
All values are means ± SDs. Dietary FFQ amounts take into account both foods and supplements. The linear regression model was used to obtain P-trend values. FFQ, food-frequency questionnaire; MET, metabolic equivalent task; PLP, pyridoxal 50-phosphate.
Associations between one-carbon metabolism plasma biomarkers and relative telomere length are shown in Table 2. There were no dose-response relations between any plasma biomarkers and relative telomere length after adjustment for batch and age or after additional adjusting for smoking, BMI, and physical activity. However, in the categorical analysis, a higher cysteine concentration was significantly inversely associated with the relative telomere length in some quartile comparisons. For example, subjects with the highest quartile of cysteine concentration had 0.72 times the odds of having an above-median telomere length than were subjects with the lowest quartile of cysteine concentration (OR: 0.72; 95% CI: 0.53, 0.98).
TABLE 2.
Multivariate analysis of one-carbon metabolism plasma biomarkers and relative telomere length1
| Plasma concentrations | Quartile 1 | Quartile 2 | Quartile 3 | Quartile 4 | P-trend |
| Folate (nmol/L) | 8.62 | 13.4 | 20.8 | 38.1 | |
| Model 1 | Reference | 0.82 (0.62, 1.09)3 | 1.10 (0.83, 1.45) | 1.03 (0.78, 1.36) | 0.90 |
| Model 2 | Reference | 0.80 (0.60, 1.07) | 1.06 (0.80, 1.41) | 1.01 (0.76, 1.34) | 0.90 |
| PLP/vitamin B-6 (pmol/mL) | 23.9 | 38.4 | 58.4 | 126.7 | |
| Model 1 | Reference | 1.16 (0.88, 1.52) | 1.06 (0.81, 1.39) | 1.10 (0.84, 1.44) | 0.98 |
| Model 2 | Reference | 1.14 (0.86, 1.51) | 1.01 (0.76, 1.33) | 1.04 (0.78, 1.38) | 0.78 |
| Vitamin B-12 (pmol/L) | 207.2 | 280.4 | 352.8 | 478.1 | |
| Model 1 | Reference | 1.11 (0.84, 1.47) | 1.15 (0.87, 1.52) | 1.21 (0.92, 1.59) | 0.97 |
| Model 2 | Reference | 1.09 (0.82, 1.45) | 1.09 (0.82, 1.45) | 1.15 (0.87, 1.53) | 0.94 |
| Cysteine (nmol/mL) | 227.9 | 265.3 | 303.2 | 369.4 | |
| Model 1 | Reference | 0.73 (0.54, 0.98) | 0.82 (0.61, 1.10) | 0.70 (0.52, 0.94) | 0.12 |
| Model 2 | Reference | 0.71 (0.53, 0.96) | 0.82 (0.60, 1.11) | 0.72 (0.53, 0.98) | 0.16 |
| Homocysteine (nmol/mL) | 7.7 | 9.7 | 11.6 | 15.0 | |
| Model 1 | Reference | 0.95 (0.72, 1.25) | 0.84 (0.64, 1.11) | 0.84 (0.63, 1.11) | 0.62 |
| Model 2 | Reference | 0.90 (0.68, 1.20) | 0.84 (0.63, 1.12) | 0.84 (0.62, 1.12) | 0.57 |
Telomere length was dichotomized at the median. Model 1 was adjusted for batch and age (y; continuous) (n = 1698). Model 2 was adjusted for batch, age (y; continuous), smoking (0, 0.1–20, 20.1–40, or >40 pack-years), BMI (in kg/m2; <25, 25–29.9, 30–34.9, or ≥35), and physical activity (nominal quartiles, metabolic equivalent tasks/wk) (n = 1624). The logistic regression model was used to obtain ORs (95% CIs), and the linear regression model was used to obtain P-trend values. PLP, pyridoxal 50-phosphate.
Median (all such values).
OR; 95% CI in parentheses (all such values).
Multivariate analyses of the FFQ-measured intakes of one-carbon metabolism dietary factors also showed no dose-response relations with the relative telomere length (Table 3), and the results for folate, vitamin B-6, and vitamin B-12 were consistent with plasma-measurement findings. Spearman's correlations between plasma-measured concentrations and FFQ-measured intakes of folate, vitamin B-6, and vitamin B-12 were 0.50, 0.44, and 0.30, respectively. Quartile median intakes could be compared with Dietary Reference Intakes of the National Academies Press (21) for women 50–70 y old (400 μg folate/d, 1.1 mg riboflavin/d, 1.5 mg vitamin B-6/d, 2.4 μg vitamin B-12/d, and 425 mg choline/d).
TABLE 3.
Multivariate analysis of FFQ-measured intakes of one-carbon metabolism dietary factors and relative telomere length1
| FFQ-measured intakes | Quartile 1 | Quartile 2 | Quartile 3 | Quartile 4 | P-trend |
| Folate (μg/d) | 229.12 | 303.9 | 409.8 | 610.6 | |
| Model 1 | Reference | 0.95 (0.70, 1.27)3 | 0.85 (0.64, 1.14) | 1.04 (0.78, 1.39) | 0.92 |
| Model 2 | Reference | 0.89 (0.66, 1.21) | 0.81 (0.60, 1.09) | 0.99 (0.73, 1.34) | 0.94 |
| Choline (mg/d) | 276.9 | 315.4 | 348.3 | 394.6 | |
| Model 1 | Reference | 1.03 (0.78, 1.37) | 0.99 (0.75, 1.32) | 0.88 (0.66, 1.17) | 0.77 |
| Model 2 | Reference | 1.05 (0.78, 1.40) | 1.04 (0.78, 1.40) | 0.93 (0.69, 1.26) | 0.72 |
| Methionine (g/d) | 1.5 | 1.7 | 1.8 | 2.1 | |
| Model 1 | Reference | 0.95 (0.72, 1.26) | 0.84 (0.64, 1.11) | 0.91 (0.68, 1.21) | 0.96 |
| Model 2 | Reference | 0.94 (0.70, 1.25) | 0.87 (0.66, 1.15) | 0.95 (0.70, 1.28) | 0.98 |
| Riboflavin (mg/d) | 1.4 | 1.9 | 2.9 | 7.7 | |
| Model 1 | Reference | 1.08 (0.80, 1.44) | 1.13 (0.85, 1.50) | 0.98 (0.74, 1.30) | 0.68 |
| Model 2 | Reference | 1.11 (0.82, 1.50) | 1.13 (0.85, 1.52) | 0.98 (0.73, 1.31) | 0.77 |
| Vitamin B-6 (mg/d) | 1.5 | 2.0 | 3.1 | 10.3 | |
| Model 1 | Reference | 0.92 (0.68, 1.25) | 0.96 (0.71, 1.31) | 0.89 (0.66, 1.20) | 0.52 |
| Model 2 | Reference | 0.89 (0.65, 1.23) | 0.93 (0.68, 1.28) | 0.89 (0.65, 1.22) | 0.71 |
| Vitamin B-12 (μg/d) | 4.5 | 7.0 | 9.8 | 14.8 | |
| Model 1 | Reference | 0.85 (0.63, 1.14) | 1.19 (0.89, 1.58) | 0.92 (0.69, 1.23) | 0.75 |
| Model 2 | Reference | 0.82 (0.61, 1.12) | 1.13 (0.84, 1.53) | 0.94 (0.70, 1.27) | 0.55 |
| Alcohol (g/d) | 0.0 | 0.9 | 4.3 | 15.6 | |
| Model 1 | Reference | 1.25 (0.93, 1.66) | 1.35 (1.02, 1.78) | 1.20 (0.91, 1.59) | 0.18 |
| Model 2 | Reference | 1.24 (0.92, 1.66) | 1.22 (0.91, 1.62) | 1.13 (0.84, 1.53) | 0.28 |
Telomere length was dichotomized at the median. Dietary Reference Intakes for women 50–70 y old were as follows: 400 μg folate/d, 1.1 mg riboflavin/d, 1.5 mg vitamin B-6/d, 2.4 μg vitamin B-12/d, and 425 mg choline/d. Model 1 was adjusted for batch and age (y; continuous) (n = 1698). Model 2 was adjusted for batch, age (y; continuous), smoking (0, 0.1–20, 20.1–40, >40 pack-years), BMI (in kg/m2; <25, 25–29.9, 30–34.9, or ≥35), and physical activity (metabolic equivalent tasks/wk; nominal quartiles) (n = 1624). The logistic regression model was used to obtain ORs (95% CIs), and the linear regression model was used to obtain P-trend values. FFQ, food-frequency questionnaire.
Median (all such values).
OR; 95% CI in parentheses (all such values).
For the SNP analysis, we showed that rs2372536 from the ATIC gene was most significantly associated with the relative telomere length (P = 0.02) (Table 4). However, the association was not statistically significant after we accounted for the false-discovery rate of SNP analyses (22).
TABLE 4.
Associations between one-carbon metabolism SNPs and relative telomere length1
| Telomere length3 |
|||||
| SNP | Gene | MAF | Reference allele2 | OR (95% CI) | P |
| rs1127717 (C, T) | ALDH1L1 | 0.20 | C | 0.94 (0.69, 1.30) | 0.72 |
| rs4646750 (C, T) | ALDH1L1 | 0.07 | C | 1.08 (0.66, 1.79) | 0.76 |
| rs2886059 (A, C) | ALDH1L1 | 0.16 | A | 1.33 (0.93, 1.90) | 0.12 |
| rs2276724 (C, T) | ALDH1L1 | 0.15 | C | 1.35 (0.94, 1.93) | 0.10 |
| rs2372536 (C, G) | ATIC | 0.32 | C | 1.39 (1.07, 1.82) | 0.02 |
| rs3733890 (A, G) | BHMT | 0.30 | A | 1.23 (0.94, 1.61) | 0.14 |
| rs9001 (G, T) | CHDH | 0.05 | G | 0.95 (0.50, 1.82) | 0.88 |
| rs12676 (A, C) | CHDH | 0.30 | A | 1.04 (0.80, 1.36) | 0.78 |
| rs1021737 (G, T) | CTH | 0.29 | G | 1.03 (0.77, 1.37) | 0.84 |
| rs2228612 (C, T) | DNMT1 | 0.06 | C | 1.22 (0.72, 2.07) | 0.47 |
| rs202676 (T, C) | FOLH1 | 0.22 | T | 1.11 (0.81, 1.52) | 0.54 |
| rs8788 (C, T) | GART | 0.17 | C | 0.99 (0.70, 1.39) | 0.93 |
| rs8971 (C, T) | GART | 0.24 | C | 1.05 (0.78, 1.42) | 0.76 |
| rs11545077 (C, T) | GGH | 0.25 | C | 0.95 (0.72, 1.24) | 0.69 |
| rs11545078 (A, G) | GGH | 0.10 | A | 1.06 (0.66, 1.69) | 0.82 |
| rs2236225 (A, G) | MTHFD1 | 0.45 | A | 1.01 (0.77, 1.31) | 0.96 |
| rs1950902 (A, G) | MTHFD2 | 0.18 | A | 1.07 (0.78, 1.46) | 0.68 |
| MTHFR 677 (C, T) | MTHFR | 0.34 | C | 0.94 (0.72, 1.24) | 0.66 |
| MTHFR 1298 (A, C) | MTHFR | 0.33 | A | 0.78 (0.60, 1.03) | 0.08 |
| rs1805087 (A, G) | MTR | 0.19 | A | 0.96 (0.70, 1.32) | 0.81 |
| rs1801394 (A, G) | MTRR | 0.47 | A | 0.83 (0.64, 1.07) | 0.15 |
| rs1532268 (C, T) | MTRR | 0.37 | C | 0.85 (0.65, 1.12) | 0.24 |
| rs2287780 (C, T) | MTRR | 0.04 | C | 0.53 (0.27, 1.06) | 0.07 |
| rs16879334 (C, G) | MTRR | 0.04 | C | 0.60 (0.31, 1.19) | 0.15 |
| rs162036 (A, G) | MTRR | 0.11 | A | 1.18 (0.78, 1.78) | 0.43 |
| rs10380 (C, T) | MTRR | 0.09 | C | 1.16 (0.75, 1.81) | 0.50 |
| rs7946 (T, C) | PEMT | 0.25 | T | 0.92 (0.70, 1.21) | 0.54 |
| rs1979277 (A, G) | SHMT1 | 0.29 | A | 0.90 (0.69, 1.18) | 0.45 |
| rs1051266 (C, T) | SLC19A1 | 0.44 | C | 0.90 (0.70, 1.16) | 0.42 |
The logistic regression model was used to obtain ORs (95% CIs) and P values. MAF, minor allele frequency; SNP, single-nucleotide polymorphism.
Homozygous genotype of the reference allele was coded 0.
Telomere length was dichotomized at the median.
DISCUSSION
In this study that involved plasma and FFQ measurements of factors in the one-carbon metabolism pathway, we showed no significant associations with telomere length for many key dietary and genetic variables.
Telomere length has been shown to be affected by DNA-methylation status, which is influenced by factors in the one-carbon metabolism pathway. DNA methylation in vertebrates occurs when DNA-methyltransferase enzymes catalyze the addition of methyl groups to cytosine residues, typically at cytosine-phosphate-guanine (CpG) islands. Because the TTAGGG hexameric repeats of the telomere lack CpG dinucleotides, methylation occurs in adjacent subtelomeres that are rich in CpG (23, 24). DNA-methyltransferase-deficient mouse embryonic stem cells have reduced methylation in subtelomeres and increased telomere length, possibly because of increased telomeric recombination (25). Telomerase-positive human tumor cell lines were shown to have increased subtelomeric methylation (26). Therefore, the relation between DNA methylation and telomere length appears to differ depending on the cell type and other underlying circumstances.
Besides DNA methylation, other biological mechanisms have been proposed for one-carbon metabolism factors to affect telomere length. During folate deficiency, there is less conversion of deoxyuridine monophosphate into dTMP, which is the precursor of deoxythymidine triphosphate. This deficient methylation of deoxyuridine monophosphate into dTMP results in increased uracil misincorporation and chromosome breaks (27). A study showed that the decreased synthesis of deoxythymidine triphosphate promotes telomere-length shortening in yeast cells, but the authors indicated that the process is independent of uracil misincorporation and is likely a result of destabilized replication forks in DNA synthesis (28). It remains to be studied whether uracil misincorporation and subsequent base excision repair are responsible for telomere shortening in other cell types.
To our knowledge, this is the first study to examine whether vitamin B-6 and cysteine biomarkers are associated with telomere length. Cysteine is an amino acid that is naturally present in foods and can be synthesized in mammals from homocysteine under methionine-sufficient conditions in a process that involves the vitamin B-6 cofactor. In previous studies of female cohorts, plasma cysteine was shown to be associated with decreased (12) and increased (29) risks of breast cancer and was not associated with myocardial infarction (30). We showed no significant association that involved plasma vitamin B-6, but in our categorical analysis, we showed that a higher plasma cysteine concentration may be significantly inversely associated with telomere length. However, our finding involving plasma cysteine needs to be cautiously interpreted because there was no significant linear trend in our multivariate analyses.
We also showed no significant associations with telomere length that involved plasma folate, homocysteine, and vitamin B-12. Two previous studies examined how plasma folate and homocysteine were associated with telomere length (4, 5). A study that involved twins in the United Kingdom showed that telomere length was positively associated with plasma folate and inversely associated with plasma homocysteine (5). In contrast, a study of Italian men showed that telomere length was nonlinearly associated with plasma folate and positively associated with plasma homocysteine (4). The Italian study also showed no association between plasma vitamin B-12 and telomere length (4).
To our knowledge, this is also the first study to evaluate associations between telomere length and dietary intakes of B vitamins, methionine, and choline, and we showed no significant associations. In addition, we examined many nonsynonymous SNPs involved in one-carbon metabolism that were previously unstudied with respect to their associations with telomere length. We showed that rs2372536 from the ATIC gene, which encodes an enzyme that converts 10-formyl tetrahydrofolate to tetrahydrofolate, was most significantly associated with telomere length. However, this finding needs to be validated because the association was not statistically significant after we accounted for the false-discovery rate (22).
Despite the strength of this study in the comprehensive assessment of associations between dietary and genetic factors in one-carbon metabolism and telomere length, there were some limitations. First, the associations in this analysis were cross-sectional and, therefore, not necessarily causal. Second, only one plasma measurement was taken per participant for dietary biomarkers, and thus, the measurement may not have reflected the biologically relevant exposure. In addition, only one measurement of telomere length was made, and thus, it was not possible to assess how changes in dietary exposure may have affected changes in telomere length. Third, although the quantitative polymerase chain reaction method we used for the measurement of telomere length was high throughput and, therefore, practically and commonly used in epidemiologic studies (31, 32), it did not provide the absolute telomere length values or percentage of short telomeres. Therefore, alternative methods that may provide more biologically relevant telomere-length measurements (31, 32) can be used in future studies of one-carbon metabolism factors and telomere length to validate our findings. Finally, the results of this study may not be generalizable to men or other ethnic groups because the women-only NHS cohort is mostly white.
In conclusion, we mostly showed no significant associations between one-carbon metabolism factors that we studied and the relative leukocyte telomere length. However, because most of these associations have not been previously evaluated, additional studies are necessary to confirm our findings. Additional biological and bioinformatic studies will help clarify the mechanisms by which one-carbon metabolism can affect telomere length.
Acknowledgments
We thank the NHS participants and staff for their contributions.
The authors’ responsibilities were as follows—JJL: conducted the statistical analysis and drafted the manuscript; JP and IDV: performed telomere-length measurements; JP, EG, SEH, BR, and IDV: critically reviewed the manuscript; and all authors: read and approved the manuscript. None of the authors had a conflict of interest.
Footnotes
Abbreviations used: CpG, cytosine-phosphate-guanine; dTMP, deoxythymidine monophosphate; FFQ, food-frequency questionnaire; NHS, Nurses’ Health Study; PLP, pyridoxal 50-phosphate; SNP, single-nucleotide polymorphism; T:S, ratio of telomere repeat copy number to a single gene copy number.
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