The correlation of salivary telomere length and single nucleotide polymorphisms of the ADIPOQ, SIRT1 and FOXO3A genes with lifestyle-related diseases in a Japanese population

Xiao Han; Ryo Kubota; Ken-ichi Tanaka; Hiroyuki Hayashi; Miyuki Seki; Nobue Sakai; Noriko Kawaguchi-Ihara; Kyoko Arakawa; Ikuo Murohashi

doi:10.1371/journal.pone.0243745

Abstract

Background

It has been reported that genetic factors are associated with risk factors and onset of lifestyle-related diseases, but this finding is still the subject of much debate.

Objective

The aim of the present study was to investigate the correlation of genetic factors, including salivary telomere length and three single nucleotide polymorphisms (SNPs) that may influence lifestyle-related diseases, with lifestyle-related diseases themselves.

Methods

In one year at a single facility, relative telomere length and SNPs were determined by using monochrome multiplex quantitative polymerase chain reaction and TaqMan SNP Genotyping Assays, respectively, and were compared with lifestyle-related diseases in 120 Japanese individuals near our university.

Results

In men and all participants, age was inversely correlated with relative telomere length with respective p values of 0.049 and 0.034. In men, the frequency of hypertension was significantly higher in the short relative telomere length group than in the long group with unadjusted p value of 0.039, and the difference in the frequency of hypertension between the two groups was of borderline statistical significance after adjustment for age (p = 0.057). Furthermore, in men and all participants, the sum of the number of affected lifestyle-related diseases, including hypertension, was significantly higher in the short relative telomere length group than in the long group, with p values of 0.004 and 0.029, respectively. For ADIPOQ rs1501299, men’s ankle brachial index was higher in the T/T genotype than in the G/G and G/T genotypes, with p values of 0.001 and 0.000, respectively. For SIRT1 rs7895833, men’s body mass index and waist circumference and all participants’ brachial-ankle pulse wave velocity were higher in the A/G genotype than in the G/G genotype, with respective p values of 0.048, 0.032 and 0.035. For FOXO3A rs2802292, women’s body temperature and all participants’ saturation of peripheral oxygen were lower in the G/T genotype than in the T/T genotype, with respective p values of 0.039 and 0.032. However, relative telomere length was not associated with physiological or anthropometric measurements except for height in men (p = 0.016). ADIPOQ rs1501299 in men, but not the other two SNPs, was significantly associated with the sum of the number of affected lifestyle-related diseases (p = 0.013), by genotype. For each SNPs, there was no significant difference in the frequency of hypertension or relative telomere length by genotype.

Conclusion

Relative telomere length and the three types of SNPs determined using saliva have been shown to be differentially associated with onset of and measured risk factors for lifestyle-related diseases consisting mainly of cardiovascular diseases and cancer.

Citation: Han X, Kubota R, Tanaka K-i, Hayashi H, Seki M, Sakai N, et al. (2021) The correlation of salivary telomere length and single nucleotide polymorphisms of the ADIPOQ, SIRT1 and FOXO3A genes with lifestyle-related diseases in a Japanese population. PLoS ONE 16(1): e0243745. https://doi.org/10.1371/journal.pone.0243745

Editor: Hoh Boon-Peng, UCSI University, MALAYSIA

Received: July 16, 2020; Accepted: November 26, 2020; Published: January 28, 2021

Copyright: © 2021 Han et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper and its Supporting Information files.

Funding: This work was supported by 2015 Graduate Student Exchange (XH) and Grant-in-Aid for Encouragement of Scientists Research Funds (IM), Saitama Prefectural University. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Telomeres are a repetitive nucleotide sequences of TTAGGG at the end of a chromatid, maintaining the stability of chromosomes to avoid deterioration and fusion with other chromosomes [1]. Telomere length (TL) is influenced by genetic factors, with previous studies reporting heritability estimates ranging from 34 to 82% [2, 3]. This parameter is also cumulatively shaped by nongenetic influences throughout human life. In contrast, single nucleotide polymorphisms (SNPs) are inherited from parents and transmit heritable events [4].

Quantitative trait locus studies have mapped putative loci that may be involved in regulating TL to human chromosomes 3q26.1, 10q26.13 and 12q12.22 [5]. Indeed, a number of recent genome-wide association studies (GWAS) identified common SNPs near the telomerase RNA component (TERC) associated with TL in European and Chinese populations [5, 6]. The strongest associations with TL were reported for the SNPs rs12696304 and rs16847897 near TERC on 3q26. Although their functions are unclear, these genes appear to be involved in the maintenance of chromosome structures [7].

Research on the genetics of human longevity has identified hundreds of genes associated with longevity, but polymorphisms in only two genes (APOE and FOXO3) have demonstrated strong and consistent replications across multiple, diverse human populations [8]. The correlation between genetic factors such as TL and SNPs and lifestyle-related diseases (LRDs) is also still controversial [8–15]. Furthermore, these studies [5, 6, 8–15] were conducted in different population cohorts, including Chinese, American and European cohorts, and in different types of studies, including original papers, literature reviews and meta-analyses.

To better understand the link between genetic factors and human disorders and diseases, we studied the correlation among TL, three SNPs that may influence the risk factors for LRDs [9, 16–18] or the onset of LRDs [19], LRD-related physiological and anthropometric measurements, and the onset of LRDs themselves in a Japanese population from limited areas near our university in one year at a single facility.

Materials and methods

Subjects

Since March 2011, an annual university-sponsored health course for community members has been conducted [20]. In each course, which was held ten times per year, health seminars and point-of-care testing were performed. In 2015, a total of 223 attendants were enrolled, and 122 out of 143 attendants in the year (85.3%) agreed to participate in an analysis of salivary TL and SNPs of the ADIPOQ, SIRT1 and FOXO3A genes. The inclusion criteria were Japanese descent, clinical stability and sufficient salivary DNA extraction for genetic factor analysis. The exclusion criterion was any acute illness. In two participants, the DNA amount was not enough for analysis, and the remaining 120 subjects were included in the present study (S1 Table). They ranged in age from 41 to 84 years and consisted of 34 men and 86 women with a mean age of 73.3 and 67.8 years, respectively. In three other healthy volunteers, TL was determined by using DNA from saliva and peripheral blood-derived leukocytes.

DNA extraction from saliva and blood

Participants expectorated at least two ml of passive drool saliva into a sterile, 50 ml polyethylene conical tube (Corning Science, Corning, NY, USA) for five min. These collection tubes were maintained on ice until use. Immediately, one ml of saliva was mixed with an equal volume of nuclei lysis buffer (50 mM Tris-HCl, 50 mM EDTA, 50 mM sucrose, 100 mM NaCl, 2.4% SDS, 550 μg/ml proteinase K, pH 8.0) [21]. After digestion of the cell lysate overnight at 53°C, 400 μl of 5 M NaCl was added to the mixture, shaken vigorously for 15 s and placed on ice for 30 min. The DNA in the supernatant was extracted by using the GenElute Mammalian Genomic DNA Miniprep Kit (Sigma-Aldrich Co., LLC., Tokyo, Japan). The concentration of the isolated DNA was determined using a NanoDrop Spectrophotometer (Thermo Scientific, Waltham, MA, USA).

Heparinized venous blood was collected from three healthy adult volunteers. Every 4 ml of blood was mixed with 1 ml of 5% (w/v) dextran (MW 266,000, Sigma-Aldrich) in phosphate-buffered saline (PBS) (Gibco, Grand Island, NY, USA), incubated for 30 min at room temperature, and the upper layer containing whole leukocytes was harvested [22]. In one volunteer, the whole leukocyte fraction in PBS was further layered onto Ficoll-Hypaque (Ficoll-Paque Plus, 1.077 g/ml, GE Healthcare Life Sciences, Buckinghamshire, England) and centrifuged at 400×g for 30 min. The whole leukocytes resolved into two distinct bands, the upper containing mononuclear cells (MNCs) and the lower containing neutrophils, and either fraction was harvested. MNCs in PBS with 10% fetal calf serum (Gibco) were further placed in Falcon culture dishes (Becton Dickinson Labware, Oxnard, CA, USA) and incubated for 2 h at 37°C in 5% CO₂. After incubation, the supernatant with the lymphocyte fraction was collected and the nonadherent cells were further removed by vigorous washing with PBS. After the addition of 3 ml fresh warm 0.05% Trypsin-EDTA (Gibco) to the flask, the cells were further incubated for five min at 37°C in 5% CO₂ and the adherent cells (monocyte fraction) were collected by tapping the side of the flask. As in the case of saliva, cellular DNA was extracted, and the concentration of the isolated DNA was determined. After blood and saliva cells bound on a glass slide by Cytospin^TM 4 Cytocentrifuge (Thermo Fisher Scientific K.K., Tokyo, Japan), Wright-Giemsa staining (Muto Pure Chemicals Co., Ltd., Tokyo, Japan) was performed, and 100 nucleated cells were counted and classified for each of the three slide glass specimens by using an inverted microscope (Eclipse E600, Nikon, Tokyo, Japan) at 1,000× magnification.

Telomere length determination by the monochrome multiplex quantitative polymerase chain reaction method

TL quantitative polymerase chain reaction (QPCR) was performed with a Chromo4^TM Real-Time PCR Detection System (Bio-Rad, Tokyo, Japan) [23]. The telomere primers (telg and telc, final concentrations 900 nM each) were used for the telomere signal and a set of single-copy gene (scg) primers within β–globin genes (final concentrations 200 nM each) were used as a reference with Power SYBR^TM Green PCR Master Mix (Applied Biosystems, Foster City, CA, USA).

The PCR cycle for monochrome multiplex QPCR (MMQPCR) was as follows: Stage 1: 1 cycle of 2 min at 95°C; Stage 2: 2 cycles of 5 s at 94°C and 15 s at 49°C; and Stage 3: 40 cycles of 5 s at 94°C, 10 s at 62°C, 15 s at 74°C with signal acquisition (for the amplification of telomere template), 10 s at 84°C, and 15 s at 88°C with signal acquisition (for the amplification of the scg template). The same standard genomic DNA was used to establish two standard curve reactions in every plate in the study, one for the telomere signal and one for the scg signal, which were used for calculation of the T/S ratios (ratios of “telomere signals per scg signals”). As each experimental sample was assayed in triplicate, three T/S results were obtained for each sample; the final reported result for a sample in a given run is the average of the three T/S values and was named relative telomere length (RTL). The average T/S is expected to be proportional to the average TL per cell. Samples with an RTL > 1.0 have an average TL greater than that of the standard DNA; samples with an RTL < 1.0 have an average TL shorter than that of the standard DNA.

TaqMan^® PCR assay

Genotyping for ADIPOQ rs1501299, Sirt-1 rs7895833 and FOXO3A rs2802292 was performed using Custom TaqMan^® SNP Genotyping Assays (Applied Biosystems) in which a fluorogenic probe consisting of an oligonucleotide labeled with both a fluorescent reporter dye (FAM or VIC) and a quencher dye is included in a typical PCR [24]. Amplification of the probe-specific product causes cleavage of the probe, generating an increase in reporter fluorescence [25]. Each primer and probe set was used in the TaqMan^® SNP Genotyping Assays (ID: C___7497299_10, C__29163689_10 and C__16097219_10; Applied Biosystems) in accordance with the information on the Applied Biosystems website (http://www.appliedbiosystems.com).

PCR was performed according to the manufacturer’s instructions provided by Applied Biosystems. In brief, one to 20 ng of template DNA dissolved in 2.25 μl in each well was loaded into 96-well plates for PCR. The total reaction volume was 5 μl after adding 2.5 μl of TaqMan Universal PCR Master Mix (2×) and 0.25 μl of 10× working stock of SNP genotyping assay buffer (Applied Biosystems).

The PCR thermal cycling was as follows: Stage 1: 1 cycle of 2 min at 50°C; Stage 2: 1cycle of 10 min at 95°C for initial denaturing; Stage 3: 40 cycles of 15 s at 92°C and 1 min at 60°C. Thermal cycling was performed using a Chromo4^TM Real-Time PCR Detection System (Bio-Rad). Each 96-well plate contained unknown genotype samples and three reaction mixtures containing the reagents but no DNA (no-template control). The no-DNA control samples were necessary for Sequence Detection System (SDS) 7700 signal processing, as outlined in the TaqMan Allelic Discrimination Guide. The genotypes were determined visually based on the dye component fluorescence emission data depicted in the X-Y scatter plot of the SDS software.

Physiological and anthropometric measurements

Body height (cm) was measured using a metal height meter (YS-OS, AS ONE Co., Ltd., Osaka, Japan). Weight (kg), body mass index [BMI (kg/m²)] and percentage of body fat (BF) (%) were measured by a dual-frequency body composition analyzer (DC 430A, Tanita Co., Ltd., Tokyo, Japan) using bioelectrical impedance analysis. Waist circumference (WC) was measured at the umbilicus to the nearest cm with the participant standing and breathing normally. Body temperature (BT) (°C) was measured using an electronic thermometer inserted into the armpit for 30 sec (ET-C205S, Terumo Co., Ltd., Tokyo, Japan).

Hypertension (HT) was defined as systolic blood pressure (SBP) ≥140 mm Hg, diastolic blood pressure (DBP) ≥90 mm Hg, or the use of antihypertensive medications [26]. After a more than 5 min rest, the SBP and DBP were measured by a trained physician or nurse using a standard mercury sphygmomanometer (SeaStar, 070108041, Tokyo, Japan) with the participant in the sitting position. Two consecutive blood pressure measurements were taken at 2-min intervals, and the lower one was recorded as the blood pressure.

Measurement of carotid maximum intima-medial thickness (max IMT) (mm) through B-mode ultrasonographic imaging was performed using C3cv (Aloka Medical, Ltd., Tokyo, Japan) with a 6-MHz transducer in the right common carotid artery 1.5 to 3.0 cm proximal to the bifurcation [27].

Measurements of the ankle brachial index (ABI) and brachial-ankle pulse wave velocity (PWV) (cm/sec) were performed as follows. Brachial-ankle arterial blood pressures were simultaneously measured using a noninvasive automatic device (model BP-203RPE-III; Nihon Colin, Tokyo, Japan) after a 5-min rest in the supine position [27]. ABI was defined as the ratio of systolic blood pressure in the ankle and the higher side of the two brachial arteries. The PWV on each side was calculated as the transmission distance divided by the transmission time. The transmission time between the right arm and both ankles was calculated using the waveform. The transmission distance between the right brachium and ankle was automatically calculated according to the height of the patient. PWV was evaluated on the higher side.

Saturation of peripheral oxygen (SpO₂) was measured using a pulse oximeter device (ko-001, Kohken Medical Co., Ltd., Tokyo, Japan).

Questionnaire survey

With the help of our trained staff, self-reported questionnaires were completed during attendance, assessing attendants’ personal medical history (PMH) and antihypertension medication use. PMH consisted of HT, stroke, acute myocardial infarction (AMI), chronic kidney disease (CKD) with therapy for edema, hyperkalemia or anemia [28] and cancer (S2 Table). The score for PMH was defined as the sum of the number of affected LRDs in PMH.

Ethical considerations

This study was approved by the Ethics Committee of Saitama Prefectural University (No. 27503). Before study enrollment, participants and normal volunteers who provided both saliva and peripheral blood samples were asked to sign a consent and assent form that described the background and procedures of the study.

Statistical analyses

Nonparametric statistics were used. Continuous variables are described as the mean ± standard deviation (SD), and categorical variables are described as proportions. Unadjusted differences between groups were performed by one-way analysis of variance (ANOVA) for continuous variables and chi-square tests for categorical variables. Multivariable linear regression analyses were used to test for adjusted differences with adjustments for the confounding effects of age and sex. Student’s t-test was performed to compare the differences in means between the two groups. A two-tailed p value less than 0.05 was used to determine significance. Statistical analyses were performed by IBM SPSS Statistics version 23.

Results

Salivary and blood relative telomere length

In any of the three volunteers studied, there was no significant difference in RTL between the DNA extracted from saliva and blood-derived whole leukocytes (S1A Fig). Similarly, in one volunteer, there was no significant difference in RTL between the DNA extracted from blood-derived neutrophils, lymphocytes and monocytes (S1B Fig).

Relative telomere length and LRD-related physiological and anthropometric measurements

First, we examined the correlation between RTL and age. Age and RTL were inversely correlated in men (p = 0.049, r = − 0.340, y = −9.258x + 82.429) and all participants (p = 0.034, r = − 0.192, y = −7.711x + 76.857) but not in women (p = 0.073, r = − 0.194, y = −8.235x + 75.766) (Fig 1 and S1 Table).

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Fig 1. Cross-sectional analysis of age-dependent shortening of salivary relative telomere length.

For each point, the age (yr) of the participant (x-axis) is plotted against salivary relative telomere length (RTL) (y-axis) for men (upper column), women (middle column), and all participants (lower column). The linear regression equation and correlation coefficient were determined using Microsoft Excel.

https://doi.org/10.1371/journal.pone.0243745.g001

Next, we examined the association between RTL and LRD-related physiological and anthropometric measurements. In men, the long RTL group (n = 17) had a significantly greater height (167.5 ± 6.6 vs 162.1 ± 5.3 cm) and had a significantly lower pulse rate (65.3 ± 12.6 vs 73.7 ± 15.5/min) than the short RTL group (n = 17) before (p = 0.011) and after (p = 0.016) and before (p = 0.041) but not after (p = 0.092) age adjustment (S3 Table). RTL was not associated with other LRD-related physiological or anthropometric measurements.

Relative telomere length and score for personal medical history

Frequencies of HT for men, women and all participants according to RTL quartiles are shown in Fig 2A. The frequency of HT decreased in the order of 1^st, 2^nd, 3^rd and 4^th RTL quartiles except for 4^th RTL quartiles for women and all participants. Longer RTL quartiles had an increasingly lower frequency of HT, especially for men. The p values for men, women and all participants after adjustments for both sex and age were 0.130, 0.723 and 0.270, respectively. In men, the frequency of HT was significantly lower in the long RTL group (3^rd and 4^th RTL quartiles) than in the short group (1^st and 2^nd RTL quartiles) with unadjusted p value of 0.039, and the difference in the frequency of HT between the two groups was of borderline statistical significance after adjustment for age (p = 0.057) (Fig 2B). The p values for women and all participants after adjustments for both sex and age were 0.597 and 0.146, respectively.

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Fig 2. Frequencies of hypertension by quartiles of salivary relative telomere length.

(A) Frequencies of hypertension (HT) for men, women and all participants in the 1^st [66.7% (6/9), 52.4% (11/21) and 56.7% (17/30), respectively], 2^nd [62.6% (5/8), 45.5% (10/22) and 50.0% (15/30), respectively], 3^rd [30.0% (3/10), 40.0% (8/20) and 36.7% (11/30), respectively] and 4^th [28.6% (2/7), 43.5% (10/23) and 40.0% (12/30), respectively] quartiles of relative telomere length (RTL) are shown. The frequency of HT was determined by using data in S1 Table. The RTL levels increase in the order of 1^st, 2^nd, 3^rd and 4^th RTL quartiles. For men, women and all participants, the p value was computed using multivariable linear regression analyses to investigate the association between frequency of HT and RTL, and was expressed after adjustments for both sex and age. (B) Frequencies of HT for men, women and all participants are also shown in two groups, one with a combination of 1^st and 2^nd RTL quartiles [64.7% (11/17), 48.8% (21/43) and 53.3% (32/60), respectively], and the other with a combination of 3^rd and 4^th RTL quartiles [29.4% (5/17), 41.9% (18/43) and 38.3% (23/60), respectively]. The p value was determined and expressed as shown in (A).

https://doi.org/10.1371/journal.pone.0243745.g002

In both men and all participants, the PMH score was also significantly higher in the short RTL group than in the long RTL group after adjustments for both age and sex with respective p values of 0.004 and 0.029 (Table 1).

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Table 1. The association of score for personal medical history with relative telomere length.

https://doi.org/10.1371/journal.pone.0243745.t001

For men, women and all participants, the score for personal medical history (PMH) was shown by relative telomere length (RTL) as shown in the materials and methods. The score for PMH was determined and is expressed as the mean ± standard deviation of the sum of the number of affected lifestyle-related diseases in PMH as shown in the materials and methods. The number of participants whose PMH score was completed was shown in parentheses. The p value was computed using multivariable linear regression analysis to investigate the association between PMH score and RTL, and was expressed after adjustments for both sex and age.

SNPs, LRD-related physiological and anthropometric measurements and scores for personal medical history

For ADIPOQ rs1501299, the ABI of men was significantly higher in carriers of the T/T genotype than in those with the G/G and G/T genotypes, with p values of 0.001 and 0.000, respectively (Table 2). For SIRT1 rs7895833, the BMI and WC of men were significantly higher in carriers of the A/G genotype than in those with the G/G genotype, with respective p values of 0.048 and 0.032. The PWV of all participants was also significantly higher in carriers of the A/G genotype than in those with the G/G genotype, with a p value of 0.035. For FOXO3A rs2802292, the BT of women and SpO₂ of all participants were significantly lower in carriers of the G/T genotype than in those with the T/T genotype, with respective p values of 0.039 and 0.032. ADIPOQ rs1501299 in men, but not the other two SNPs, was significantly associated with the PMH score by genotype (p = 0.013).

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Table 2. The association of SNPs with LRD-related physiological and anthropometric measurements and scores for personal medical history.

https://doi.org/10.1371/journal.pone.0243745.t002

In men, women and all participants, the results for LRD-related physiological and anthropometric measurements, RTL and frequency of HT not associated with any of the three SNPs by genotype are shown in S4 Table.

Reproducibility of salivary relative telomere length measurements

Finally, to examine inter-assay reproducibility, we repeated the measurements of salivary RTL in the same 120 DNA samples as in Fig 1, again in triplicate, on another day. Fig 3 shows the strong correlation between the salivary RTL determined by the first and second runs (R² = 0.9061).

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Fig 3. Reproducibility of salivary relative telomere length in independent runs.

The same 120 salivary DNA samples assayed in Fig 1 were assayed again on different days. The linear regression equation and correlation coefficient were determined using Microsoft Excel.

https://doi.org/10.1371/journal.pone.0243745.g003

Discussion

We successfully determined RTL and genotypes of the three SNPs by using DNA from passive drool saliva in any of the 120 participants. However, caution is warranted when comparing TL measured from saliva obtained through passive drool and saliva obtained using swabs or sponges as the percentage of buccal cells in the latter is significantly higher than that of the former [29]. We confirmed in three volunteers that there was no significant difference in RTL measured using passive drool saliva and blood-derived whole leukocytes. In one volunteer, the proportion of neutrophils in passive drool saliva was as high as 78.27 ± 6.03% (mean ± SD of triplicate measurements). To date, a growing body of evidence has shown that TL measurement using passive drool saliva instead of blood-derived whole leukocytes is quite useful [30–32].

As previously reported [33], the long RTL group (3^rd plus 4^th) had a significantly lower frequency of HT than the short group (1^st plus 2^nd) only in men with unadjusted p value of 0.039, and the difference was of borderline statistical significance after age adjustment (p = 0.057). Furthermore, compatible with a previous report [34], our present study found a significant association between the PMH score and RTL in men and all participants. In the present study, LRDs consisted mainly of cardiovascular diseases and cancer. Inconsistent with previous reports [35, 36], however, RTL did not correlate with SBP, DBP or pulse pressure because of the frequent use of antihypertension medication in the present study (40/120; 33.3%).

The overall effect of individually unique environmental factors during adult life, such as energy intake, lifestyle, socioeconomic status and mental stress, is relatively small compared with the joint effect on TL of heritability and shared environmental factors, which is estimated at ~87% [37]. In addition, in recent years, medical treatment and preventive medicine have made remarkable progress [38]. Interestingly, the Mendelian randomization approach, which usually avoids the potential confounding effect of environmental exposure on the relation between phenotypically measured TL and risk of ischemic stroke, has shown that genetically predicted TL is not associated with ischemic stroke [8]. The effect of heritability on TL is presumed to be weaker in the middle-aged to elderly subjects in the present study than in the younger subjects. For these reasons, in the present study as well, it seems likely that RTL appears to be more strongly associated with the combination of heritability and shared environmental factors than heritability or individual-specific environmental factors alone, in terms of predisposition to the development of LRDs.

In the present study, men in the long RTL group were shown to be significantly taller than those in the short RTL group. Although it is undeniable that bone mineral density may be maintained with long TL [39], it would be more correct to say that height is not a risk factor for LRDs.

Associations between the SNPs and LRD-related physiological or anthropometric measurements in the present study were as follows: for ADIPOQ rs1501299, in accordance with previous papers [16, 19], men’s ABI was higher in the T/T genotype than in the G/G or G/T genotype; for SIRT1 rs7895833, consistent with previous papers [17, 18], men’s BMI and waist circumference and all participants’ PWV were higher in the A/G genotype than in the G/G genotype; for FOXO3A rs2802292, women’s BT and all participants’ SpO₂ were lower in the G/T genotype than in the T/T genotype. We confirmed again in the peresent study that the Japanese allele frequencies for SIRT1 rs7895833 [17], which are different from those of Caucasians [18], might explain why Japanese individuals show less marked obesity than Caucasians. Consistent with a role for FOXO3 in suppression of insulin/insulin-like growth factor and thus growth to extend lifespan, the longevity-associated G allele variant of FOXO3A rs2802292 seems to be associated with lower BT and SpO₂ [9, 40].

Compatible with previous reports [16, 19], only ADIPOQ rs1501299, but not the other two types of SNPs showed a significant correlation with both risk factors for and PMH score of LRDs. In any of the three SNPs, there was no significant difference in the frequency of HT or RTL by genotype.

Many studies have shown that TL is a heritable trait, and SNPs in several candidate genes including TERT (telomerase reverse transcriptase), TERC (telomerase RNA component), OBFC1 (oligonucleotide/oligosaccharide-binding folds containing one), CTC1 (conserved telomere maintenance component 1) and ZNF676 (zinc fingerprotein676) have been identified to be significantly associated with TL [2, 41]. These genes have limited genetic variations and encode proteins that are thought to be involved either in TL maintenance or with telomere binding proteins necessary for telomere stability and structure. Exceptionally, fat mass- and obesity-associated (FTO) genes have been shown to be associated with obesity and TL [7], and the FOXO3 G allele of SNP rs2802292 significantly protected against aged-related TL loss relative to that of carriers of the common TT genotype [42]. However, we could not find any association between RTL and genotype in any of the three SNPs examined in the present study.

SNPs are the most common type of human genetic variation and have been associated with disease development and phenotypic forecasting [43]. Although GWAS identified genetic variants involved in complex phenotypes, the fraction of heritability of common traits and diseases explained by the identified loci is small [44]. The reason for this small proportion of the heritability of these complex traits is still unclear, but the causes can involve epistatic effects, genetic interactions inside undiscovered pathway or underestimated genotype-environment interactions [45, 46].

SIRT1 also regulates a stress-response transcription factor, FOXO3, thereby modulating cellular senescence/aging, skeletal muscle function, cardiovascular homeostasis, and human longevity [9, 40, 47]. Furthermore, telomeres have been shown to be closely associated with sirtuin [48] and FOXO3 [42]. Therefore, further studies are required to clarify the exact correlation between the two SNPs and TL.

DNA methylation is capable of controlling the gene expression of common traits and influencing the development of aberrant health outcomes under external exposure [49]. Recently, an epigenetic ‘mortality risk score’ based on whole blood DNA methylation at 10 mortality-related CpG sites has been shown to be strongly associated with TL and even more strongly associated with all-cause mortality [50]. However, TL failed to correlate with all-cause mortality. DNA methylation studies of the SNPs may further clarify the relationship among SNPs, TL and LRDs [51].

In the present study, the frequency of the significant association of genetic factors with LRDs was higher in men than in women. It is presumed that the reason is, in part, that men have a higher prevalence of LRDs, especially cardiovascular disease, and a shorter life expectancy than women in specific age groups, 50–70 [52]. The number of subjects in this study was small, 34 men and 86 women, with a total of 120, so the results of the study may be limited; however, none of the 34 men were excluded (S1 Table). In any case, further studies are required to confirm our present results.

Conclusion

Over the course of a year and in a single facility, we showed that RTL and the three types of SNPs determined using saliva are differentially associated with onset of and measured risk factors for LRDs consisting mainly of cardiovascular diseases and cancer in Japanese individuals living in a small area near our university.

Supporting information

S1 Fig. Salivary and blood relative telomere length.

https://doi.org/10.1371/journal.pone.0243745.s001

(DOCX)

S1 Table. Participants’ characteristics, relative telomere length and personal medical histories.

https://doi.org/10.1371/journal.pone.0243745.s002

(DOCX)

S2 Table. Request for personal medical history.

https://doi.org/10.1371/journal.pone.0243745.s003

(DOCX)

S3 Table. Association between relative telomere length and LRD-related physiological and anthropometric measurements.

https://doi.org/10.1371/journal.pone.0243745.s004

(DOCX)

S4 Table. LRD-related physiological and anthropometric measurements, relative telomere length and frequency of hypertension not associated with any of the three SNPs by genotype.

https://doi.org/10.1371/journal.pone.0243745.s005

(DOCX)

Acknowledgments

We thank our lab members for supporting the annual university-sponsored course for community members. We also thank Dr. Yukio Kamezawa (Saitama Prefectural University) for generously providing cell-separation reagents and for helpful discussions on this study.

References

1. Moon I, Jarstfer M. The human telomere and its relationship to human disease, therapy, and tissue engineering. Front Biosci. 2007;12:4595–4620. pmid:17485399
- View Article
- PubMed/NCBI
- Google Scholar
2. Lee JH, Cheng R, Honig LS, Feitosa M, Kammerer CM, Kang MS, et al. Genome wide association and linkage analyses identified three loci-4q25, 17q23.2, and 10q11.21-associated with variation in leukocyte telomere length: the Long Life Family Study. Front Genet. 2014;4:310. pmid:24478790
- View Article
- PubMed/NCBI
- Google Scholar
3. Blackburn EH, Epel ES, Lin J. Human telomere biology: A contributory and interactive factor in aging, disease risks, and protection. Science. 2015;350(6265):1193–1198. pmid:26785477
- View Article
- PubMed/NCBI
- Google Scholar
4. Hemminki K, Försti A, Lorenzo Bermejo J. Single nucleotide polymorphisms (SNPs) are inherited from parents and they measure heritable events. J Carcinog. 2005;4(1):2. pmid:15652000
- View Article
- PubMed/NCBI
- Google Scholar
5. Codd V, Mangino M, van der Harst P, Braund PS, Kaiser M, Beveridge AJ, et al. Common variants near TERC are associated with mean telomere length. Nat Genet. 2010;42(3):197–199. pmid:20139977
- View Article
- PubMed/NCBI
- Google Scholar
6. Shen Q, Zhang Z, Yu L, Cao L, Zhou D, Kan MY, et al. Common variants near TERC are associated with leukocyte telomere length in the Chinese Han population. Eur J Hum Genet. 2011;19(6):721–723. pmid:21304559
- View Article
- PubMed/NCBI
- Google Scholar
7. Zhou Y, Hambly BD, McLachlan CS. FTO associations with obesity and telomere length. J Biomed Sci. 2017;24(1):65. pmid:28859657
- View Article
- PubMed/NCBI
- Google Scholar
8. Cao W, Li X, Zhang X, Zhang J, Sun Q, Xu X, et al. No Causal Effect of Telomere Length on Ischemic Stroke and Its Subtypes: A Mendelian Randomization Study. Cells. 2019;8(2):159. pmid:30769869
- View Article
- PubMed/NCBI
- Google Scholar
9. Morris BJ, Willcox DC, Donlon TA, Willcox BJ. FOXO3: a major gene for human longevity–a mini-review. Gerontology. 2015;61(6):515–525. pmid:25832544
- View Article
- PubMed/NCBI
- Google Scholar
10. Schürks M, Prescott J, Dushkes R, De Vivo I, Rexrode KM. Telomere length and ischaemic stroke in women: a nested case-control study. Eur J Neurol. 2013;20(7):1068–1074. pmid:23521613
- View Article
- PubMed/NCBI
- Google Scholar
11. Jin X, Pan B, Dang X, Wu H, Xu D. Relationship between short telomere length and stroke: A meta-analysis. Medicine (Baltimore). 2018;97(39):e12489. pmid:30278538
- View Article
- PubMed/NCBI
- Google Scholar
12. Neuner B, Lenfers A, Kelsch R, Jäger K, Brüggmann N, van der Harst P, et al. Telomere Length Is Not Related to Established Cardiovascular Risk Factors but Does Correlate with Red and White Blood Cell Counts in a German Blood Donor Population. PLoS ONE. 2015;10(10):e0139308. pmid:26445269
- View Article
- PubMed/NCBI
- Google Scholar
13. Sun Q, Liu J, Cheng G, Dai M, Liu J, Qi Z, et al. The telomerase gene polymorphisms, but not telomere length, increase susceptibility to primary glomerulonephritis/end stage renal diseases in females. J Transl Med. 2020;18(1):184. pmid:32366311
- View Article
- PubMed/NCBI
- Google Scholar
14. Luu H, Huang J, Wang R, Adams-Haduch J, Jin A, Koh W, et al. Association between leukocyte telomere length and the risk of pancreatic cancer: Findings from a prospective study. PLoS ONE. 2019;14(8):e0221697. pmid:31465482
- View Article
- PubMed/NCBI
- Google Scholar
15. Bao Y, Prescott J, Yuan C, Zhang M, Kraft P, Babic A, et al. Leukocyte telomere length, genetic variants at the TERT gene region, and risk of pancreatic cancer. Gut. 2017;66(6):1116–1122. pmid:27797938
- View Article
- PubMed/NCBI
- Google Scholar
16. Jang Y, Lee JH, Chae JS, Kim OY, Koh SJ, Kim JY, et al. Association of the 276G->T polymorphism of the adiponectin gene with cardiovascular disease risk factors in nondiabetic Koreans. Am J Clin Nutr. 2005;82(4):760–767. pmid:16210704
- View Article
- PubMed/NCBI
- Google Scholar
17. Shimoyama Y, Suzuki K, Hamajima N, Niwa T. Sirtuin 1 gene polymorphisms are associated with body fat and blood pressure in Japanese. Transl Res. 2011;157(6):339–347. pmid:21575918
- View Article
- PubMed/NCBI
- Google Scholar
18. Zillikens MC, van Meurs JB, Rivadeneira F, Amin N, Hofman A, Oostra BA, et al. SIRT1 genetic variation is related to BMI and risk of obesity. Diabetes. 2009;58(12):2828–2834. pmid:19741164
- View Article
- PubMed/NCBI
- Google Scholar
19. Hara K, Boutin P, Mori Y, Tobe K, Dina C, Yasuda K, et al. Genetic variation in the gene encoding adiponectin is associated with an increased risk of type 2 diabetes in the Japanese population. Diabetes. 2002;51(2):536–540. pmid:11812766
- View Article
- PubMed/NCBI
- Google Scholar
20. Arakawa K, Sakai N, Ihara N, Kubota R, Uchiyama M, Seki M, et al. Effectiveness of single urine sample method to estimate and reduce daily salt intake at university-sponsored health seminars. Rinsho Byori. 2018;66(4):371–375.
- View Article
- Google Scholar
21. Quinque D, Kittler R, Kayser M, Stoneking M, Nasidze I. Evaluation of saliva as a source of human DNA for population and association studies. Anal Biochem. 2006;353(2):272–277. pmid:16620753
- View Article
- PubMed/NCBI
- Google Scholar
22. Ferrante A, Beard LJ, Thong YH. Early decay of human neutrophil chemotactic responsiveness following isolation from peripheral blood. Clin Exp Immunol. 1980;39(2):532–537. pmid:7389208
- View Article
- PubMed/NCBI
- Google Scholar
23. Cawthon RM. Telomere length measurement by a novel monochrome multiplex quantitative PCR method. Nucleic Acids Res. 2009;37(3):e21. pmid:19129229
- View Article
- PubMed/NCBI
- Google Scholar
24. Osaki R, Imaeda H, Ban H, Aomatsu T, Bamba S, Tsujikawa T, et al. Accuracy of genotyping using the TaqMan PCR assay for single nucleotide polymorphisms responsible for thiopurine sensitivity in Japanese patients with inflammatory bowel disease. Exp Ther Med. 2011;2(5):783–786. pmid:22977575
- View Article
- PubMed/NCBI
- Google Scholar
25. Hui L, DelMonte T, Ranade K. Genotyping using the TaqMan assay. Curr Protoc Hum Genet. 2008;Chapter 2:Unit 2.10. pmid:18428424
- View Article
- PubMed/NCBI
- Google Scholar
26. Shimamoto K, Ando K, Fujita T, Hasebe N, Higaki J, Horiuchi M, et al. The Japanese Society of Hypertension Guidelines for the Management of Hypertension (JSH 2014). Hypertens Res. 2014;37(4):253–390. pmid:24705419
- View Article
- PubMed/NCBI
- Google Scholar
27. Kanazawa I, Yamaguchi T, Yamamoto M, YamauchiM, Kurioka S, Yano S, et al. Serum DHEA-S level is associated with the presence of atherosclerosis in postmenopausal women with type 2 diabetes mellitus. Endocr J. 2008;55(4):667–675. pmid:18493102
- View Article
- PubMed/NCBI
- Google Scholar
28. Heerspink HL, Zeeuw D. Novel drugs and intervention strategies for the treatment of chronic kidney disease. Br J Clin Pharmacol. 2013;76(4):536–550. pmid:23802504
- View Article
- PubMed/NCBI
- Google Scholar
29. Lin J, Smith DL, Esteves K, Drury S. Telomere length measurement by qPCR–Summary of critical factors and recommendations for assay design. Psychoneuroendocrinology. 2019;99:271–278. pmid:30343983
- View Article
- PubMed/NCBI
- Google Scholar
30. Zhang C, Lauderdale DS, Pierce BL. Sex-Specific and Time-Varying Associations Between Cigarette Smoking and Telomere Length Among Older Adults. Am J Epidemiol. 2016;184(12):922–932. pmid:27856448
- View Article
- PubMed/NCBI
- Google Scholar
31. Zou Y, Leong W, Yao M, Hu X, Lu S, Zhu X, et al. Test anxiety and telomere length: Academic stress in adolescents may not cause rapid telomere erosion. Oncotarget. 2017;8(7):10836–10844. pmid:28122333
- View Article
- PubMed/NCBI
- Google Scholar
32. Ridout KK, Ridout SJ, Guille C, Mata DA, Akil H, Sen S. Physician-Training Stress and Accelerated Cellular Aging. Biol Psychiatry. 2019;86(9):725–730. pmid:31230727
- View Article
- PubMed/NCBI
- Google Scholar
33. Tellechea ML, Pirola CJ. The impact of hypertension on leukocyte telomere length: a systematic review and meta-analysis of human studies. J Hum Hypertens. 2017;31(2):99–105. pmid:27357526
- View Article
- PubMed/NCBI
- Google Scholar
34. Fasching CL. Telomere length measurement as a clinical biomarker of aging and disease. Crit Rev Clin Lab Sci. 2018;55(7):443–465. pmid:30265166
- View Article
- PubMed/NCBI
- Google Scholar
35. Insel KC, Merkle CJ, Hsiao CP, Vidrine AN, Montgomery DW. Biomarkers for cognitive aging part I: telomere length, blood pressure and cognition among individuals with hypertension. Biol Res Nurs. 2012;14(2):124–132. pmid:21586494
- View Article
- PubMed/NCBI
- Google Scholar
36. Jeanclos E, Schork NJ, Kyvik KO, Kimura M, Skurnick JH, Aviv A. Telomere length inversely correlates with pulse pressure and is highly familial. Hypertension. 2000;36(2):195–200. pmid:10948077
- View Article
- PubMed/NCBI
- Google Scholar
37. Hjelmborg JB, Dalgård C, Möller S, Steenstrup T, Kimura M, Christensen K, et al. The heritability of leucocyte telomere length dynamics. J Med Genet. 2015;52(5):297–302. pmid:25770094
- View Article
- PubMed/NCBI
- Google Scholar
38. Takahashi PY, Okhravi HR, Lim LS, Kasten MJ. Preventive health care in the elderly population: a guide for practicing physicians. Mayo Clin Proc. 2004;79(3):416–427. pmid:15008616
- View Article
- PubMed/NCBI
- Google Scholar
39. Bekaert S, Van Pottelbergh I, De Meyer T, Zmierczak H, Kaufman JM, Van Oostveldt P, et al. Telomere length versus hormonal and bone mineral status in healthy elderly men. Mech Ageing Dev. 2005;126(10):1115–1122. pmid:15967485
- View Article
- PubMed/NCBI
- Google Scholar
40. Willcox BJ, Morris BJ, Tranah GJ, Chen R, Masaki KH, He Q, et al. Longevity-Associated FOXO3 Genotype and its Impact on Coronary Artery Disease Mortality in Japanese, Whites, and Blacks: A Prospective Study of Three American Populations. J Gerontol A Biol Sci Med Sci. 2017;72(5):724–728. pmid:27694344
- View Article
- PubMed/NCBI
- Google Scholar
41. Mirabello L, Yu K, Kraft P, De Vivo I, Hunter DJ, Prescott J, et al. The association of telomere length and genetic variation in telomere biology genes. Hum Mutat. 2010;31(9):1050–1058. pmid:20597107
- View Article
- PubMed/NCBI
- Google Scholar
42. Davy PMC, Willcox DC, Shimabukuro M, Donlon TA, Torigoe T, Suzuki M, et al. Minimal Shortening of Leukocyte Telomere Length Across Age Groups in a Cross-Sectional Study for Carriers of a Longevity-Associated FOXO3 Allele. J Gerontol A Biol Sci Med Sci. 2018;73(11):1448–1452. pmid:29688278
- View Article
- PubMed/NCBI
- Google Scholar
43. Giampaoli S, Chillemi G, Valeriani F, Lazzaro D, Borro M, Gentile G, et al. The SNPs in the human genetic blueprint era. N Biotechnol. 2013;30(5):475–484. pmid:23183540
- View Article
- PubMed/NCBI
- Google Scholar
44. Huang J, Liu EL, Welch R, Willer C, Hindorff LA, et al. WikiGWA: an open platform for collecting and using genome-wide association results. Eur J Hum Genet. 2013;21(4),471–473. pmid:22929026
- View Article
- PubMed/NCBI
- Google Scholar
45. Stranger BE, Stahl EA, Raj T. Progress and promise of genome-wide association studies for human complex trait genetics. Genetics. 2011;187(2):367–383. pmid:21115973
- View Article
- PubMed/NCBI
- Google Scholar
46. Zuk O, Hechter E, Sunyaev SR, Lander ES. The mystery of missing heritability: Genetic interactions create phantom heritability. Proc Natl Acad Sci U S A. 2012;109(4):1193–1198. pmid:22223662
- View Article
- PubMed/NCBI
- Google Scholar
47. Brunet A, Sweeney LB, Sturgill JF, Chua KF, Greer PL, Lin Y, et al. Stress-dependent regulation of FOXO transcription factors by the SIRT1 deacetylase. Science. 2004;303(5666):2011–2015. pmid:14976264
- View Article
- PubMed/NCBI
- Google Scholar
48. Amano H, Chaudhury A, Rodriguez-Aguayo C, Lu L, Akhanov V, Catic A, et al. Telomere dysfunction induces sirtuin repression that drives telomere-dependent disease. Cell Metab. 2019;29(6):1274–1290. pmid:30930169
- View Article
- PubMed/NCBI
- Google Scholar
49. Ladd-Acosta C. Epigenetic signatures as biomarkers of exposure. Curr Environ Health Rep. 2015;2(2):117–125. pmid:26231361
- View Article
- PubMed/NCBI
- Google Scholar
50. Gao X, Zhang Y, Mons U, Brenner H. Leukocyte telomere length and epigenetic-based mortality risk score: associations with all-cause mortality among older adults. Epigenetics. 2018;13(8):846–857. pmid:30152726
- View Article
- PubMed/NCBI
- Google Scholar
51. Wang H, Lou D, Wang Z. Crosstalk of Genetic Variants, Allele-Specific DNA Methylation, and Environmental Factors for Complex Disease Risk. Front Genet. 2019;9:695. pmid:30687383
- View Article
- PubMed/NCBI
- Google Scholar
52. Beltrán-Sánchez H, Finch CE, Crimmins EM. Twentieth century surge of excess adult male mortality. Proc Natl Acad Sci U S A. 2015;112(29):8993–8998. pmid:26150507
- View Article
- PubMed/NCBI
- Google Scholar

[ref1] 1. Moon I, Jarstfer M. The human telomere and its relationship to human disease, therapy, and tissue engineering. Front Biosci. 2007;12:4595–4620. pmid:17485399
View Article
PubMed/NCBI
Google Scholar

[2] View Article

[3] PubMed/NCBI

[4] Google Scholar

[ref2] 2. Lee JH, Cheng R, Honig LS, Feitosa M, Kammerer CM, Kang MS, et al. Genome wide association and linkage analyses identified three loci-4q25, 17q23.2, and 10q11.21-associated with variation in leukocyte telomere length: the Long Life Family Study. Front Genet. 2014;4:310. pmid:24478790
View Article
PubMed/NCBI
Google Scholar

[6] View Article

[7] PubMed/NCBI

[8] Google Scholar

[ref3] 3. Blackburn EH, Epel ES, Lin J. Human telomere biology: A contributory and interactive factor in aging, disease risks, and protection. Science. 2015;350(6265):1193–1198. pmid:26785477
View Article
PubMed/NCBI
Google Scholar

[10] View Article

[11] PubMed/NCBI

[12] Google Scholar

[ref4] 4. Hemminki K, Försti A, Lorenzo Bermejo J. Single nucleotide polymorphisms (SNPs) are inherited from parents and they measure heritable events. J Carcinog. 2005;4(1):2. pmid:15652000
View Article
PubMed/NCBI
Google Scholar

[14] View Article

[15] PubMed/NCBI

[16] Google Scholar

[ref5] 5. Codd V, Mangino M, van der Harst P, Braund PS, Kaiser M, Beveridge AJ, et al. Common variants near TERC are associated with mean telomere length. Nat Genet. 2010;42(3):197–199. pmid:20139977
View Article
PubMed/NCBI
Google Scholar

[18] View Article

[19] PubMed/NCBI

[20] Google Scholar

[ref6] 6. Shen Q, Zhang Z, Yu L, Cao L, Zhou D, Kan MY, et al. Common variants near TERC are associated with leukocyte telomere length in the Chinese Han population. Eur J Hum Genet. 2011;19(6):721–723. pmid:21304559
View Article
PubMed/NCBI
Google Scholar

[22] View Article

[23] PubMed/NCBI

[24] Google Scholar

[ref7] 7. Zhou Y, Hambly BD, McLachlan CS. FTO associations with obesity and telomere length. J Biomed Sci. 2017;24(1):65. pmid:28859657
View Article
PubMed/NCBI
Google Scholar

[26] View Article

[27] PubMed/NCBI

[28] Google Scholar

[ref8] 8. Cao W, Li X, Zhang X, Zhang J, Sun Q, Xu X, et al. No Causal Effect of Telomere Length on Ischemic Stroke and Its Subtypes: A Mendelian Randomization Study. Cells. 2019;8(2):159. pmid:30769869
View Article
PubMed/NCBI
Google Scholar

[30] View Article

[31] PubMed/NCBI

[32] Google Scholar

[ref9] 9. Morris BJ, Willcox DC, Donlon TA, Willcox BJ. FOXO3: a major gene for human longevity–a mini-review. Gerontology. 2015;61(6):515–525. pmid:25832544
View Article
PubMed/NCBI
Google Scholar

[34] View Article

[35] PubMed/NCBI

[36] Google Scholar

[ref10] 10. Schürks M, Prescott J, Dushkes R, De Vivo I, Rexrode KM. Telomere length and ischaemic stroke in women: a nested case-control study. Eur J Neurol. 2013;20(7):1068–1074. pmid:23521613
View Article
PubMed/NCBI
Google Scholar

[38] View Article

[39] PubMed/NCBI

[40] Google Scholar

[ref11] 11. Jin X, Pan B, Dang X, Wu H, Xu D. Relationship between short telomere length and stroke: A meta-analysis. Medicine (Baltimore). 2018;97(39):e12489. pmid:30278538
View Article
PubMed/NCBI
Google Scholar

[42] View Article

[43] PubMed/NCBI

[44] Google Scholar

[ref12] 12. Neuner B, Lenfers A, Kelsch R, Jäger K, Brüggmann N, van der Harst P, et al. Telomere Length Is Not Related to Established Cardiovascular Risk Factors but Does Correlate with Red and White Blood Cell Counts in a German Blood Donor Population. PLoS ONE. 2015;10(10):e0139308. pmid:26445269
View Article
PubMed/NCBI
Google Scholar

[46] View Article

[47] PubMed/NCBI

[48] Google Scholar

[ref13] 13. Sun Q, Liu J, Cheng G, Dai M, Liu J, Qi Z, et al. The telomerase gene polymorphisms, but not telomere length, increase susceptibility to primary glomerulonephritis/end stage renal diseases in females. J Transl Med. 2020;18(1):184. pmid:32366311
View Article
PubMed/NCBI
Google Scholar

[50] View Article

[51] PubMed/NCBI

[52] Google Scholar

[ref14] 14. Luu H, Huang J, Wang R, Adams-Haduch J, Jin A, Koh W, et al. Association between leukocyte telomere length and the risk of pancreatic cancer: Findings from a prospective study. PLoS ONE. 2019;14(8):e0221697. pmid:31465482
View Article
PubMed/NCBI
Google Scholar

[54] View Article

[55] PubMed/NCBI

[56] Google Scholar

[ref15] 15. Bao Y, Prescott J, Yuan C, Zhang M, Kraft P, Babic A, et al. Leukocyte telomere length, genetic variants at the TERT gene region, and risk of pancreatic cancer. Gut. 2017;66(6):1116–1122. pmid:27797938
View Article
PubMed/NCBI
Google Scholar

[58] View Article

[59] PubMed/NCBI

[60] Google Scholar

[ref16] 16. Jang Y, Lee JH, Chae JS, Kim OY, Koh SJ, Kim JY, et al. Association of the 276G->T polymorphism of the adiponectin gene with cardiovascular disease risk factors in nondiabetic Koreans. Am J Clin Nutr. 2005;82(4):760–767. pmid:16210704
View Article
PubMed/NCBI
Google Scholar

[62] View Article

[63] PubMed/NCBI

[64] Google Scholar

[ref17] 17. Shimoyama Y, Suzuki K, Hamajima N, Niwa T. Sirtuin 1 gene polymorphisms are associated with body fat and blood pressure in Japanese. Transl Res. 2011;157(6):339–347. pmid:21575918
View Article
PubMed/NCBI
Google Scholar

[66] View Article

[67] PubMed/NCBI

[68] Google Scholar

[ref18] 18. Zillikens MC, van Meurs JB, Rivadeneira F, Amin N, Hofman A, Oostra BA, et al. SIRT1 genetic variation is related to BMI and risk of obesity. Diabetes. 2009;58(12):2828–2834. pmid:19741164
View Article
PubMed/NCBI
Google Scholar

[70] View Article

[71] PubMed/NCBI

[72] Google Scholar

[ref19] 19. Hara K, Boutin P, Mori Y, Tobe K, Dina C, Yasuda K, et al. Genetic variation in the gene encoding adiponectin is associated with an increased risk of type 2 diabetes in the Japanese population. Diabetes. 2002;51(2):536–540. pmid:11812766
View Article
PubMed/NCBI
Google Scholar

[74] View Article

[75] PubMed/NCBI

[76] Google Scholar

[ref20] 20. Arakawa K, Sakai N, Ihara N, Kubota R, Uchiyama M, Seki M, et al. Effectiveness of single urine sample method to estimate and reduce daily salt intake at university-sponsored health seminars. Rinsho Byori. 2018;66(4):371–375.
View Article
Google Scholar

[78] View Article

[79] Google Scholar

[ref21] 21. Quinque D, Kittler R, Kayser M, Stoneking M, Nasidze I. Evaluation of saliva as a source of human DNA for population and association studies. Anal Biochem. 2006;353(2):272–277. pmid:16620753
View Article
PubMed/NCBI
Google Scholar

[81] View Article

[82] PubMed/NCBI

[83] Google Scholar

[ref22] 22. Ferrante A, Beard LJ, Thong YH. Early decay of human neutrophil chemotactic responsiveness following isolation from peripheral blood. Clin Exp Immunol. 1980;39(2):532–537. pmid:7389208
View Article
PubMed/NCBI
Google Scholar

[85] View Article

[86] PubMed/NCBI

[87] Google Scholar

[ref23] 23. Cawthon RM. Telomere length measurement by a novel monochrome multiplex quantitative PCR method. Nucleic Acids Res. 2009;37(3):e21. pmid:19129229
View Article
PubMed/NCBI
Google Scholar

[89] View Article

[90] PubMed/NCBI

[91] Google Scholar

[ref24] 24. Osaki R, Imaeda H, Ban H, Aomatsu T, Bamba S, Tsujikawa T, et al. Accuracy of genotyping using the TaqMan PCR assay for single nucleotide polymorphisms responsible for thiopurine sensitivity in Japanese patients with inflammatory bowel disease. Exp Ther Med. 2011;2(5):783–786. pmid:22977575
View Article
PubMed/NCBI
Google Scholar

[93] View Article

[94] PubMed/NCBI

[95] Google Scholar

[ref25] 25. Hui L, DelMonte T, Ranade K. Genotyping using the TaqMan assay. Curr Protoc Hum Genet. 2008;Chapter 2:Unit 2.10. pmid:18428424
View Article
PubMed/NCBI
Google Scholar

[97] View Article

[98] PubMed/NCBI

[99] Google Scholar

[ref26] 26. Shimamoto K, Ando K, Fujita T, Hasebe N, Higaki J, Horiuchi M, et al. The Japanese Society of Hypertension Guidelines for the Management of Hypertension (JSH 2014). Hypertens Res. 2014;37(4):253–390. pmid:24705419
View Article
PubMed/NCBI
Google Scholar

[101] View Article

[102] PubMed/NCBI

[103] Google Scholar

[ref27] 27. Kanazawa I, Yamaguchi T, Yamamoto M, YamauchiM, Kurioka S, Yano S, et al. Serum DHEA-S level is associated with the presence of atherosclerosis in postmenopausal women with type 2 diabetes mellitus. Endocr J. 2008;55(4):667–675. pmid:18493102
View Article
PubMed/NCBI
Google Scholar

[105] View Article

[106] PubMed/NCBI

[107] Google Scholar

[ref28] 28. Heerspink HL, Zeeuw D. Novel drugs and intervention strategies for the treatment of chronic kidney disease. Br J Clin Pharmacol. 2013;76(4):536–550. pmid:23802504
View Article
PubMed/NCBI
Google Scholar

[109] View Article

[110] PubMed/NCBI

[111] Google Scholar

[ref29] 29. Lin J, Smith DL, Esteves K, Drury S. Telomere length measurement by qPCR–Summary of critical factors and recommendations for assay design. Psychoneuroendocrinology. 2019;99:271–278. pmid:30343983
View Article
PubMed/NCBI
Google Scholar

[113] View Article

[114] PubMed/NCBI

[115] Google Scholar

[ref30] 30. Zhang C, Lauderdale DS, Pierce BL. Sex-Specific and Time-Varying Associations Between Cigarette Smoking and Telomere Length Among Older Adults. Am J Epidemiol. 2016;184(12):922–932. pmid:27856448
View Article
PubMed/NCBI
Google Scholar

[117] View Article

[118] PubMed/NCBI

[119] Google Scholar

[ref31] 31. Zou Y, Leong W, Yao M, Hu X, Lu S, Zhu X, et al. Test anxiety and telomere length: Academic stress in adolescents may not cause rapid telomere erosion. Oncotarget. 2017;8(7):10836–10844. pmid:28122333
View Article
PubMed/NCBI
Google Scholar

[121] View Article

[122] PubMed/NCBI

[123] Google Scholar

[ref32] 32. Ridout KK, Ridout SJ, Guille C, Mata DA, Akil H, Sen S. Physician-Training Stress and Accelerated Cellular Aging. Biol Psychiatry. 2019;86(9):725–730. pmid:31230727
View Article
PubMed/NCBI
Google Scholar

[125] View Article

[126] PubMed/NCBI

[127] Google Scholar

[ref33] 33. Tellechea ML, Pirola CJ. The impact of hypertension on leukocyte telomere length: a systematic review and meta-analysis of human studies. J Hum Hypertens. 2017;31(2):99–105. pmid:27357526
View Article
PubMed/NCBI
Google Scholar

[129] View Article

[130] PubMed/NCBI

[131] Google Scholar

[ref34] 34. Fasching CL. Telomere length measurement as a clinical biomarker of aging and disease. Crit Rev Clin Lab Sci. 2018;55(7):443–465. pmid:30265166
View Article
PubMed/NCBI
Google Scholar

[133] View Article

[134] PubMed/NCBI

[135] Google Scholar

[ref35] 35. Insel KC, Merkle CJ, Hsiao CP, Vidrine AN, Montgomery DW. Biomarkers for cognitive aging part I: telomere length, blood pressure and cognition among individuals with hypertension. Biol Res Nurs. 2012;14(2):124–132. pmid:21586494
View Article
PubMed/NCBI
Google Scholar

[137] View Article

[138] PubMed/NCBI

[139] Google Scholar

[ref36] 36. Jeanclos E, Schork NJ, Kyvik KO, Kimura M, Skurnick JH, Aviv A. Telomere length inversely correlates with pulse pressure and is highly familial. Hypertension. 2000;36(2):195–200. pmid:10948077
View Article
PubMed/NCBI
Google Scholar

[141] View Article

[142] PubMed/NCBI

[143] Google Scholar

[ref37] 37. Hjelmborg JB, Dalgård C, Möller S, Steenstrup T, Kimura M, Christensen K, et al. The heritability of leucocyte telomere length dynamics. J Med Genet. 2015;52(5):297–302. pmid:25770094
View Article
PubMed/NCBI
Google Scholar

[145] View Article

[146] PubMed/NCBI

[147] Google Scholar

[ref38] 38. Takahashi PY, Okhravi HR, Lim LS, Kasten MJ. Preventive health care in the elderly population: a guide for practicing physicians. Mayo Clin Proc. 2004;79(3):416–427. pmid:15008616
View Article
PubMed/NCBI
Google Scholar

[149] View Article

[150] PubMed/NCBI

[151] Google Scholar

[ref39] 39. Bekaert S, Van Pottelbergh I, De Meyer T, Zmierczak H, Kaufman JM, Van Oostveldt P, et al. Telomere length versus hormonal and bone mineral status in healthy elderly men. Mech Ageing Dev. 2005;126(10):1115–1122. pmid:15967485
View Article
PubMed/NCBI
Google Scholar

[153] View Article

[154] PubMed/NCBI

[155] Google Scholar

[ref40] 40. Willcox BJ, Morris BJ, Tranah GJ, Chen R, Masaki KH, He Q, et al. Longevity-Associated FOXO3 Genotype and its Impact on Coronary Artery Disease Mortality in Japanese, Whites, and Blacks: A Prospective Study of Three American Populations. J Gerontol A Biol Sci Med Sci. 2017;72(5):724–728. pmid:27694344
View Article
PubMed/NCBI
Google Scholar

[157] View Article

[158] PubMed/NCBI

[159] Google Scholar

[ref41] 41. Mirabello L, Yu K, Kraft P, De Vivo I, Hunter DJ, Prescott J, et al. The association of telomere length and genetic variation in telomere biology genes. Hum Mutat. 2010;31(9):1050–1058. pmid:20597107
View Article
PubMed/NCBI
Google Scholar

[161] View Article

[162] PubMed/NCBI

[163] Google Scholar

[ref42] 42. Davy PMC, Willcox DC, Shimabukuro M, Donlon TA, Torigoe T, Suzuki M, et al. Minimal Shortening of Leukocyte Telomere Length Across Age Groups in a Cross-Sectional Study for Carriers of a Longevity-Associated FOXO3 Allele. J Gerontol A Biol Sci Med Sci. 2018;73(11):1448–1452. pmid:29688278
View Article
PubMed/NCBI
Google Scholar

[165] View Article

[166] PubMed/NCBI

[167] Google Scholar

[ref43] 43. Giampaoli S, Chillemi G, Valeriani F, Lazzaro D, Borro M, Gentile G, et al. The SNPs in the human genetic blueprint era. N Biotechnol. 2013;30(5):475–484. pmid:23183540
View Article
PubMed/NCBI
Google Scholar

[169] View Article

[170] PubMed/NCBI

[171] Google Scholar

[ref44] 44. Huang J, Liu EL, Welch R, Willer C, Hindorff LA, et al. WikiGWA: an open platform for collecting and using genome-wide association results. Eur J Hum Genet. 2013;21(4),471–473. pmid:22929026
View Article
PubMed/NCBI
Google Scholar

[173] View Article

[174] PubMed/NCBI

[175] Google Scholar

[ref45] 45. Stranger BE, Stahl EA, Raj T. Progress and promise of genome-wide association studies for human complex trait genetics. Genetics. 2011;187(2):367–383. pmid:21115973
View Article
PubMed/NCBI
Google Scholar

[177] View Article

[178] PubMed/NCBI

[179] Google Scholar

[ref46] 46. Zuk O, Hechter E, Sunyaev SR, Lander ES. The mystery of missing heritability: Genetic interactions create phantom heritability. Proc Natl Acad Sci U S A. 2012;109(4):1193–1198. pmid:22223662
View Article
PubMed/NCBI
Google Scholar

[181] View Article

[182] PubMed/NCBI

[183] Google Scholar

[ref47] 47. Brunet A, Sweeney LB, Sturgill JF, Chua KF, Greer PL, Lin Y, et al. Stress-dependent regulation of FOXO transcription factors by the SIRT1 deacetylase. Science. 2004;303(5666):2011–2015. pmid:14976264
View Article
PubMed/NCBI
Google Scholar

[185] View Article

[186] PubMed/NCBI

[187] Google Scholar

[ref48] 48. Amano H, Chaudhury A, Rodriguez-Aguayo C, Lu L, Akhanov V, Catic A, et al. Telomere dysfunction induces sirtuin repression that drives telomere-dependent disease. Cell Metab. 2019;29(6):1274–1290. pmid:30930169
View Article
PubMed/NCBI
Google Scholar

[189] View Article

[190] PubMed/NCBI

[191] Google Scholar

[ref49] 49. Ladd-Acosta C. Epigenetic signatures as biomarkers of exposure. Curr Environ Health Rep. 2015;2(2):117–125. pmid:26231361
View Article
PubMed/NCBI
Google Scholar

[193] View Article

[194] PubMed/NCBI

[195] Google Scholar

[ref50] 50. Gao X, Zhang Y, Mons U, Brenner H. Leukocyte telomere length and epigenetic-based mortality risk score: associations with all-cause mortality among older adults. Epigenetics. 2018;13(8):846–857. pmid:30152726
View Article
PubMed/NCBI
Google Scholar

[197] View Article

[198] PubMed/NCBI

[199] Google Scholar

[ref51] 51. Wang H, Lou D, Wang Z. Crosstalk of Genetic Variants, Allele-Specific DNA Methylation, and Environmental Factors for Complex Disease Risk. Front Genet. 2019;9:695. pmid:30687383
View Article
PubMed/NCBI
Google Scholar

[201] View Article

[202] PubMed/NCBI

[203] Google Scholar

[ref52] 52. Beltrán-Sánchez H, Finch CE, Crimmins EM. Twentieth century surge of excess adult male mortality. Proc Natl Acad Sci U S A. 2015;112(29):8993–8998. pmid:26150507
View Article
PubMed/NCBI
Google Scholar

[205] View Article

[206] PubMed/NCBI

[207] Google Scholar

Figures

Abstract

Background

Objective

Methods

Results

Conclusion

Introduction

Materials and methods

Subjects

DNA extraction from saliva and blood

Telomere length determination by the monochrome multiplex quantitative polymerase chain reaction method

TaqMan® PCR assay

Physiological and anthropometric measurements

Questionnaire survey

Ethical considerations

Statistical analyses

Results

Salivary and blood relative telomere length

Relative telomere length and LRD-related physiological and anthropometric measurements

Relative telomere length and score for personal medical history

SNPs, LRD-related physiological and anthropometric measurements and scores for personal medical history

Reproducibility of salivary relative telomere length measurements

Discussion

Conclusion

Supporting information

S1 Fig. Salivary and blood relative telomere length.

S1 Table. Participants’ characteristics, relative telomere length and personal medical histories.

S2 Table. Request for personal medical history.

S3 Table. Association between relative telomere length and LRD-related physiological and anthropometric measurements.

S4 Table. LRD-related physiological and anthropometric measurements, relative telomere length and frequency of hypertension not associated with any of the three SNPs by genotype.

Acknowledgments

References

TaqMan^® PCR assay