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Hyperglycemia attenuates the association between telomere length and age in Ukrainian population
Experimental Gerontology 110 (2018) 247–252
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Hyperglycemia attenuates the association between telomere length and age in Ukrainian population
T
Dmytro S. Krasnienkova, Mykola D. Khalangotb,c, Victor I. Kravchenkob, Volodymyr A. Kovtunb, Vitaly G. Guryanovd, Valentina P. Chizhovaa, Oleg V. Korkushkoa, Valery B. Shatiloa, ⁎ Vitaly M. Kukharskya, Alexander M. Vaisermana, a
Chebotariov Institute of Gerontology NAMS of Ukraine, Kyiv 04114, Ukraine Komisarenko Institute of Endocrinology and Metabolism NAMS of Ukraine, Kyiv 04114, Ukraine c Department of Endocrinology, Shupyk National Medical Academy of Postgraduate Education, Kyiv 04112, Ukraine d Department of Medical and Biological Physics and Informatics, Bogomolets National Medical University, Kyiv 02000, Ukraine b
A R T I C LE I N FO
A B S T R A C T
Section Editor: Holly M. Brown-Borg
Diabetes-related conditions such as chronic hyperglycemia and related oxidative stress and inflammation were repeatedly associated with accelerated telomere shortening in epidemiological studies, although some findings are inconsistent. In present study, we aimed to assess the impact of disturbances in glucose metabolism on association between age and leukocyte telomere length (LTL) in the Ukrainian population. The study was conducted on the 119 adult subjects aged between 43 and 87 years residing in the Kyiv region, Ukraine. LTL was determined by a quantitative PCR-based method. LTL was negatively correlated with the measure of abdominal obesity such as waist-hip ratio, as well as with both fasting plasma glucose (FPG) and two-hour post-load glucose (2hPG) levels. Consistently with previous studies, a significant negative association between LTL and age was observed in individuals with normal (< 5.6 mmol/L) FPG levels. Unexpectedly, however, no association was found in subjects with impaired glucose metabolism assessed by abnormal FPG or/and 2hPG levels. No association between LTL and age was observed in a logistic regression model; the association between LTL and age became significant after adjusting for FPG level. In the FPG-adjusted model, 1.6-time lower odds to have long telomere length were indicated for each 10 years increase in age. We hypothesize that the attenuation of association between LTL and age in hyperglycemic persons can likely be attributed to the interaction of multidirectional processes determining this relationship.
Keywords: Type 2 diabetes Hyperglycemia Inflammation Oxidative stress Aging Telomere length
1. Introduction Life expectancy has been significantly extended worldwide during the last century. Such increase of life expectancy is, however, not accompanied by corresponding improvement in health span as most present-day societies undergo rapid population aging (Vaiserman and Lushchak, 2017). Since aging is the main risk factor for most chronic disorders, the incidence of age-related pathologies including type 2 diabetes (T2D) rises to a large extent with increasing longevity. In recent decades, T2D is emerged as an epidemic worldwide. About 9% of the global adult population (around 415 million people in total) presently has diabetes; this number is expected to increase dramatically
and will reach 642 million people over the next decade (Chatterjee et al., 2017; Jaacks et al., 2016). T2D, accounting for > 90% of all diabetes cases, is commonly referred to as typical age-related disease. Its age-specific incidence and mortality rates rise exponentially with age, starting at age 40 and doubling with each successive 6–8-year period. As a result, about 20% of people over the age of 65 have T2D (Perry 3rd, 1999; Samos and Roos, 1998). The risk factors involved in the etiology of this disorder include genetic predisposition, unhealthy dietary habits, inadequate physical activity and stresses. Pathophysiological mechanisms of T2D include impaired β-cell function, peripheral insulin resistance and disturbed glucose metabolism (Skyler et al., 2017). Hyperglycemia is a common feature of T2D. One of the most
Abbreviations: 2hPG, two-hour post-load glucose; ADA, American Diabetes Association; AFG, abnormal fasting glucose; BMI, body mass index; DiastBP, diastolic blood pressure; FPG, fasting plasma glucose; IFG, impaired fasting glucose; IGT, impaired glucose tolerance; HC, hip circumference; LTL, leukocyte telomere length; NFG, normal fasting glucose; NGT, normal glucose tolerance; RTL, relative telomere length; SystBP, Systolic blood pressure; T2D, type 2 diabetes; TERT, telomerase reverse transcriptase; WC, waist circumference; WHO, World Health Organization; WHR, waist-hip ratio ⁎ Corresponding author at: Institute of Gerontology NAMS of Ukraine, Vyshgorodska str. 67, Kyiv 04114, Ukraine. E-mail address: [email protected] (A.M. Vaiserman). https://doi.org/10.1016/j.exger.2018.06.027 Received 23 January 2018; Received in revised form 23 June 2018; Accepted 25 June 2018 Available online 26 June 2018 0531-5565/ © 2018 Elsevier Inc. All rights reserved.
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details, see Shatylo et al., 2016]. 82 persons were recruited under the supervision of the Institute of Endocrinology and Metabolism from two local family medicine clinics of the Makariv rural district (Kyiv region) [for more sample details, see Khalangot et al. (2016, 2017a, b)]. The inclusion criteria were as follows: 1) middle-to-old age persons (minimal age: 43; maximum age: 87); 2) lack of previously diagnosed T2D. Thus, all participants involved in the study did not receive glucose-lowering drugs before the investigation started. This is an important point because treatment with such medications may likely influence LTLs, thereby biasing the results of the study [see, e.g., Ma et al., 2015]. The exclusion criteria were as follows: 1) residence in a region other than Kyiv; 2) inability to visit the clinic due to either chronic illness or disability; 3) refusal or inability to give informed consent.
important consequences of chronic hyperglycemia is oxidative stress leading, in turn, to chronic inflammation (Tangvarasittichai, 2015) and accelerated telomere shortening (Tamura et al., 2016a). Since all these processes are known to accompany aging, T2D is considered by several authors as a premature aging syndrome (Murillo-Ortiz et al., 2012). Telomeres are DNA-protein complexes that cap and protect the ends of eukaryotic chromosomes (Blackburn et al., 2015). The length of telomeres is regulated by a specific RNA-dependent DNA polymerase complex, telomerase, which catalyzes the addition of telomeric repeats to the ends of chromosomes to preserve their integrity. In most somatic cells, telomeric regions shorten during successive cell divisions (a process referred to as “telomere attrition”) due to insufficient telomerase activity. Therefore, cellular replication may go on until a critical threshold of telomere length is reached (Bonfigli et al., 2016). For this reason, the rate of telomere shortening is considered to be an indicator of replicative senescence, and telomere length, especially leukocyte telomere length (LTL), is widely used as a biomarker of organismal aging (Blackburn et al., 2015; Khan et al., 2017). Due to their chemical composition, telomeres are highly vulnerable to oxidative damage, and chronic oxidative stress, in particular that related to T2D and its complications, can accelerate telomeric shortening (Koliada et al., 2015). Based on the available data, it can be assumed that chronic hyperglycemia, oxidative stress, and telomere attrition in different tissues, including pancreatic beta cells and adipocytes, can be key components of a vicious cycle underlying the pathophysiology of T2D (Tamura et al., 2016a). The association of T2D and related conditions with short telomere length has been observed in many epidemiological studies. For example, LTLs have been shown to be significantly lower in pre-diabetic subjects with impaired glucose tolerance (IGT), lower still in T2D individuals without atherosclerotic plaques and lowest in T2D patients with atherosclerotic plaques compared to control subjects (Adaikalakoteswari et al., 2007). Shortened telomeres were also found in β-cells from autopsy pancreas obtained from T2D patients compared to age-matched control individuals (Tamura et al., 2014). In this study, telomere lengths were negatively correlated with glycated hemoglobin levels and telomeres were substantially shorter in T2D patients who had been treated with hypoglycemic medications than in those who had been not, indicating that an association exists between T2D severity and telomere attrition rate. A relationship between T2D and telomere length was evident in recent meta-analyses on this topic (D'Mello et al., 2015; Wang et al., 2016; Willeit et al., 2014; Zhao et al., 2013). This relationship was, however, significantly influenced by age, sex, body mass index (BMI), region of residence and diabetes type (Wang et al., 2016). Moreover, reports on the association of telomere length with T2D and associated cardiometabolic risk factors are conflicting in the literature (Zhao et al., 2013). For example, leukocyte telomere length has not been associated with T2D status and duration, as well as with poor glucose control in diabetic patients in the US general population (Menke et al., 2015). In our previous study, LTL was inversely associated with two-hour post-load glucose (2hPG) levels but not with fasting plasma glucose (FPG) levels (Khalangot et al., 2017a, b). Due to such inconsistency in the results, further studies need to be conducted for better understanding of causal relationships and pathways involved in this association. In present study, we aimed to assess the impact of disturbances in glucose metabolism on association between age and telomere length in Ukrainian population.
2.2. Ethical aspects The study protocol was approved by the Ethics Committees of the Institute of Endocrinology and Metabolism and Institute of Gerontology (both are part of the National Academy of Medical Sciences of Ukraine). All participants provided written informed consent. The Declaration of Helsinki (2000) and the applicable national standards as they relate to the involvement of human subjects in research were enforced. 2.3. Collection and storage of blood samples For the FPG test, blood was collected from all volunteers after a 10 h fast. For the oral glucose tolerance test, blood was collected for 2 h after ingestion of glucose (75 g of glucose per 200 mL of water) in the morning after at least 10 h of fasting. Blood was collected in EDTAcoated tubes and centrifuged at 1000g for 10 min. For DNA extraction, blood was collected in EDTA-coated tubes and stored at −80 °C until DNA extraction procedure. 2.4. Measurement of baseline characteristics BMI was determined as the body weight (in kg) divided by the height (in m) squared (kg/m2). Waist circumference (WC) was measured at the point of noticeable waist narrowing using a flexible anthropometric tape, with the subject in a standing position. Hip circumference (HC) was measured at the maximum circumference over the buttocks. The waist-hip ratio (WHR) was calculated by the ratio between WC and HC. Systolic blood pressure (SystBP) and diastolic blood pressure (DiastBP) (mm/Hg) were measured twice with a standard sphygmomanometer in a sitting position after at least 5 min of rest. Plasma glucose levels were determined by a standard glucose oxidase method. Among all subjects studied, 32 were categorized as normal fasting glucose (NFG) persons (FPG < 5.6 mmol/L), and 87 were categorized as abnormal fasting glucose (AFG) individuals (FPG ≥ 5.6 mmol/L), among them, 68 had impaired fasting glucose (IFG) levels (5.6–6.9 mmol/L, prediabetic persons) and 19 had diabetic (DIAB) FPG levels (> 6.9 mmol/L). According to the 2hPG test, 90 subjects had normal 2hPG levels (< 7.8 mmol/L; normal glucose tolerance, NGT), while 29 were categorized as abnormal glucose tolerance (AGT) persons (≥7.8 mmol/L), among them, 22 had prediabetic (IGT; 7.8–11.0 mmol/L) and 7 had diabetic (> 11.0 mmol/L) 2hPG levels. 2.5. Telomere length assay
2. Methods
The relative telomere lengths (RTLs) were measured by monochrome multiplex polymerase chain reaction in real time (qPCR) following the method described by Cawthon (2009). DNA was extracted from the whole blood by the phenol-chloroform purification method (Greider and Blackburn, 1985). PCR reaction mix was prepared using a commercial reagent kit for RT-PCR (Syntol, Russia) with addition of betaine (Sigma, USA) at a final concentration of 1 M. For multiplex
2.1. Participants 119 eligible participants were recruited for the study during the 2014 to 2016 timeframe. Among them, 37 persons were recruited from the clinic of Institute of Gerontology, Kyiv, Ukraine [for more sample 248
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shown). Such a trend change of the age-related dynamics of FPG in older age groups was observed in several other studies, e.g., in the study by Khan et al. (2012). This paradoxical change of the age-related trend can likely be explained by healthy lifestyle changes and/or by selection bias among elderly persons. T/S ratio was negatively correlated with a measure of abdominal obesity such as WHR. This result coincides with that obtained in American Indians (Chen et al., 2014). Expectedly, a significant negative correlation has been found between T/S ratio and both indicators of glucose metabolism (FPG and 2hPG) investigated. No relationship was found between LTL and age (R2 = 0.03, p = 0.07) (Fig. 1A). LTL was not associated with FPG levels in NFG subjects (R2 = 0.00002, p = 0.98) and was slightly associated in AFG subjects (R2 = 0.07, p = 0.02) (Fig. 1B). Slight association was found between LTL and 2hPG levels in NGT individuals (R2 = 0.055, p = 0.02); no such association was seen for AGT individuals (R2 = 0.12, p = 0.07) (Fig. 1C). T/S ratio was found to be dependent on FPG category (JT test: z = −2.25, p = 0.025), see also Fig. 2A. No significant association between T/S ratio and post-load (2hPG) categories was revealed (p > 0.05), while significant association trend was observed across different combinations of fasting and post-load glucose categories (JT test: z = −2.18, p = 0.029; Fig. 2B). Unexpectedly, no association of T/S ratio with age was observed in the sample studied (Table 2). We further examined this relationship in different FPG and 2hPG categories in order to investigate whether such an association exists in persons with normal and elevated blood glucose levels. Since two different cut-off values are used now to define the impaired fasting glycaemia state [World Health Organization (WHO): ≥ 6.1 mmol/L; American Diabetes Association (ADA): ≥ 5.6 mmol/L] (Yip et al., 2017), both these cut-off values were used in evaluating the results of the FPG test. A strongly significant correlation [even according to a higher criteria for statistical significance such as p < 0,005 recently proposed for reproducibility of research results (Benjamin et al., 2018)] between LTL (T/S ratio) and age was observed in subjects with normal FPG levels (Table 3); this association was weaker but still significant (Spearman r = −0.28, p = 0.02) if less strict cut-off (< 6.1 mmol/L) was used. No association of LTL with age was detected in persons with elevated FPG levels as well as with both normal and elevated 2hPG levels (Table 3). In studying relationship between T/S ratio and age in persons with normal glucose metabolism and with different combinations of abnormal fasting glucose and/or glucose tolerance, a highly significant correlation was found in the normal glucose (‘NFG AND NGT’) group, whereas no association was observed in both AFG or/and AGT groups (Table 4). Although a borderline significance only was observed for difference in median LTL values between NFG and AFG categories (Mann-Whitney test, p = 0.06), strongly differing relationship patterns between LTL and age were found within these categories. A clear difference in LTL levels was observed between different age groups in NFG persons; no such difference was found for AFG individuals (Fig. 2C).
qPCR, the telomere primer pair telg and telc (final concentrations 450 nM each) were combined with the albumin primer pair albu and albd (final concentrations 250 nM each) in the master mix. The thermal cycling profile was as follows: 15 min at 95 °C; 2 cycles of 15 s at 94 °C, 15 s at 49 °C; and 32 cycles of 15 s at 94 °C, 10 s at 62 °C, 15 s at 74 °C with signal acquisition, 10 s at 84 °C, and 15 s at 88 °C with signal acquisition. To obtain the calibration curve, PCR was carried out at four concentrations of the reference DNA in duplicates which cover a range of 27-fold dilutions, prepared by serial dilution. All DNA samples were run in triplicate. Amplification curves were generated by the Opticon Monitor 3 software. After the thermal cycling and raw data collection was completed, the Opticon Monitor 3 software was used to generate two standard curves for each plate, one for the telomere signal and another for the single-copy gene (scg) albumin signal. The telomere length was expressed as the T/S ratio, the telomere repeat copy number (T) to the scg copy number (S). 2.6. Statistical analysis The Shapiro-Wilk test was used to check the normality of variable distributions. Both parametric and non-parametric tests were used for statistical analyses. The parametric Pearson's or non-parametric Spearman's correlation tests were used depending on the normality of the data to investigate relationships between variables. Pairwise comparisons between means were performed using Student's t-test and comparisons between medians were performed using Mann–Whitney U test. The significance of changes in LTL (T/S ratio) over glucose categories or over age groups was tested by the Jonckheere-Terpstra (JT) trend test. Multivariate logistic regression models were used to estimate odds ratios (OR) and 95% confidence intervals (CIs) for the association between T/S ratio (dependent variable) and age (independent variable), accounting for potential confounders. Analyses were performed by Statistica 8.0 (StatSoft Inc., USA) and Medcalc v. 18 (MedCalc Software, Belgium) softwares. 3. Results The demographic, anthropometric and clinical characteristics of the studied population are presented in the Table 1. As we can see from the table, the anthropometric parameters BMI, WC and WHR were normally distributed (marked in bold in the table), allowing parametric statistical tests to be performed. For the parameters BMI, HC and WHR, statistically significant differences between sexes have been obtained; those differences were absent for other parameters studied including the relative LTL (T/S ratio). Correlations among parameters investigated are presented in Table 2. Surprisingly, FPG levels were negatively correlated with age. The direction of this association was negative in the total sample studied because FPG levels were positively related to age up to about 60 years of age and then steadily declined across age groups (data not Table 1 Baseline characteristics of the studied population. Parameter
All (n = 119)
Male (n = 35)
Female (n = 84)
p (M vs. F)
Age, yrs SystBP, mm Hg DiastBP, mm Hg FPG, mmol/L 2hPG, mmol/L LTL, T/S ratio HC, cm BMI, kg/m2 WC, cm WHR
62.0 (57.0–68.0) 135 (125–155) 84 (80–95) 6.0 (5.5–6.7) 6.4 (5.1–7.6) 0.61 (0.50–0.72) 110 (105–122) 31.91 ( ± 6.37) 102.3 ( ± 14.4) 0.93 ( ± 0.08)
60.0 (54.0–66.8) 126 (122–147) 82 (80–92) 6.0 (5.3–6.6) 6.1 (5.0–8.6) 0.61 (0.49–0.79) 101 (98–109) 29.44 ( ± 3.85) 101.1 ( ± 8.9) 0.97 ( ± 0.06)
62.0 (57.0–69.5) 140 (126–155) 84 (77–95) 6.0 (5.5–6.7) 6.5 (5.1–7.6) 0.61 (0.50–0.71) 110 (105–122) 32.9 ( ± 6.91) 102.8 ( ± 16.1) 0.91 ( ± 0.08)
0.21a 0.06a 0.88a 0.75a 0.72a 0.35a < 0.001a 0.007b 0.48b < 0.001b
Notes: Normally distributed characteristics (marked in bold) are expressed as mean ± standard deviation (SD) values. Non-normally distributed characteristics are expressed as medians (Q1–Q3). Medians are compared by the Mann–Whitney U testa and means are compared by the Student t-testb. 249
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Table 2 Correlation matrix between measured variables.
T/S Age WC HC WHR BMI FPG 2hPG SistBP DiastBP
T/S
Age
WC
HC
WHR
BMI
FPG
2hPG
SistBP
DiastBP
1 – – – −0.22 – −0.24 −0.24 – –
1 – – 0.20 – −0.24 – 0.32 –
1 0.79 0.58⁎ 0.85⁎ 0.40 – 0.29 –
1 – – 0.33 – – –
1 0.28⁎ 0.23 – – –
1 0.39 – 0.21 –
1 0.24 – –
1 – –
1 0.58
1
Notes: Correlations are determined by Spearman or Pearson tests depending on the normality of the data. Statistically significant correlations only (p < 0.05) are shown. Significant Pearson correlation coefficients are marked with asterisks. Non-significant correlations are marked with dashes.
Fig. 1. Relationship plots for various fasting glucose and post-load glucose categories. (A) Gross profile of relationship between LTL (T/S ratio) and age (n = 119). (B) Relationship plots for various fasting glucose categories: NFG: normal fasting glucose (n = 32); AFG: abnormal fasting glucose (n = 87); (C) Relationship plots for various 2hPG categories: NGT: normal glucose tolerance (n = 90); AGT: abnormal glucose tolerance (n = 29).
Fig. 2. Box-and-whisker plots for various fasting or post-load glucose categories and for different age groups. (A) Individual data points and box-and-whisker plots for various fasting glucose categories. (B) Individual data points and box-and-whisker plots for various combinations of fasting and post-load glucose categories. (C) Individual data points and box-and-whisker plots for different age groups. In each box-and-whisker plot, the box represents the values from the lower to upper quartile (25 to 75 percentile). The middle line inside the box represents the median. The whiskers above and below the box show the locations of the minimum and maximum values. Data points outside of boxplot whiskers are outliers, defined as being beyond 1.5 interquartile range of each quartile. NFG: normal fasting glucose (n = 32); AFG: abnormal fasting glucose (n = 87); IFG: impaired fasting glucose (n = 68); DIAB – diabetes (n = 19); NGT: normal glucose tolerance (n = 90); AGT: abnormal glucose tolerance (n = 29). NFG AND NGT: n = 23; AFG OR AGT: n = 76; AFG AND AGT: n = 20. Age categories: NFG: 40–59, n = 6; 60–79, n = 19; ≥80, n = 7; AFG: 40–59, n = 39; 60–79, n = 45; ≥80, n = 3.
The impact of potential confounders on the association between T/S ratio and age was evaluated by multivariate logistic regression model. In a univariate (unadjusted) model, no association between T/S ratio and age was observed (Table 5). After stepwise selection of covariates, only age remained significant predictor and was included in the final adjusted model. After adjusting for FPG level, the association between T/S ratio and age became significant. In the FPG-adjusted model, the odds to have long telomere length (defined as being in the highest tertile of FPG) decreased significantly with age. In this model, 1.6-time lower odds to have long telomere length were indicated for each 10 years increase in age.
Table 3 Spearman rank-order correlations between LTL and age in persons with normal and elevated fasting blood glucose levels. Type of blood glucose test
Cut-off value, mmol/L
N
Spearman r
p-Level
FPG
< 5,6 ≥5,6 < 7,8 ≥7,8
32 87 90 29
−0.55 −0.05 −0.05 −0.18
0.001 0.65 0.61 0.33
2hPG
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inflammation-related telomerase activation in inflammatory cells and changes in leukocyte composition, on the other hand, would bias the association between telomere length and age. This can be especially important in older age groups commonly characterized by a chronic, low-grade inflammation (“inflammaging”), which is an important risk factor for most if not all age-related disorders (Franceschi and Campisi, 2014). It may, in particular, provide a potential explanation for the decline in the rate of telomere attrition with age (Frenck Jr et al., 1998; Zeichner et al., 1999; Aubert et al., 2012; Tamura et al., 2016b), as well as for a paradoxical age-related telomere elongation observed in several older age groups in both cross-sectional and longitudinal studies (Arai et al., 2015; Berglund et al., 2016). In view of these considerations, we plan to investigate in our further research whether both inflammationinduced telomerase activation and altered leukocyte composition can affect the association between LTL and age. The limitation of our research is that cross-sectional design used in the study precludes any strong conclusions on causality. Therefore, longitudinal observation should be conducted further to verify our findings. Finally, it should be noted that the sample size of present study is too small to draw definitive conclusions. Therefore, the reported findings should be regarded as preliminary only and need further evaluation in larger samples.
Table 4 Spearman rank-order correlations between LTL and age in persons with normal glucose metabolism and with abnormal fasting glucose and/or glucose tolerance levels. Category
Results of glucose tests, mmol/L
N
Spearman r
p-Level
‘NFG AND NGT’ group ‘AFG OR AGT’ group ‘AFG AND AGT’ group
FPG < 5.6; 2hPG < 7.8
23
−0.56
0.01
FPG ≥ 5.6 OR 2hPG ≥ 7.8 FPG ≥ 5.6; 2hPG ≥ 7.8
76 20
−0.10 0.15
0.43 0.54
Table 5 Unadjusted and adjusted logistic regression model estimates of association between T/S ratio and age. Variable a
Age (unit = 10 years) Age (unit = 10 years)b
β coefficient ± S.E.
OR
95% CI
p
0.32 ± 0.20 0.44 ± 0.21
1.37 1.56
0.92–2.04 1.02–2.38
0.12 0.04
Notes: aunivariate model; bFPG-adjusted (FPG unit = 1 mmol/L) model.
4. Discussion Conflict of interests
To our knowledge, our study is the first to examine the association of LTL with age in persons with normal and abnormal glucose metabolism. Consistently with previous studies, negative association between LTL and age was observed in individuals with normal FPG levels. Unexpectedly, however, no association was found in subjects with impaired glucose metabolism assessed by subnormal FPG levels. We hypothesized that such attenuation of relationship can be attributed to superposition of opposite effects determining this association. On the one hand, hyperglycemia-related oxidative stress and related inflammatory processes can accelerate age-related telomere attrition (Koliada et al., 2015). For example, a higher systemic inflammatory load was shown to be associated with short LTL in an older population (O'Donovan et al., 2011). On the other hand, inflammation can likely induce telomerase activity thereby causing the telomere elongation. For example, telomerase was shown to be transiently expressed in human T lymphocytes upon activation (Roth et al., 2003). Supporting evidence comes from the study conducted in persons with metabolic syndrome, a group of metabolic risk factors such as IGT, insulin resistance, dyslipidemia, elevated blood pressure and obesity (Rentoukas et al., 2012). In this research, an increased telomerase activity was observed in circulating peripheral blood mononuclear cells, along with elevated markers of inflammation and endothelial dysfunction in these persons. The enhancement of telomerase activity was also found in macrophages by the inflammatory remodeling mediating atherosclerosis formation in the low-density lipoprotein receptor-deficient mice model (Gizard et al., 2011). In this study, inflammatory stimuli resulted in telomerase activation in macrophages by inducing the expression of the catalytic subunit telomerase reverse transcriptase (TERT). Restricted glucose conditions, on the contrary, were found to be associated with a significant decrease in the telomerase activity associated with a significant reduction in the TERT expression in breast cancer cells (Wardi et al., 2014). An important point is that, since chronic inflammation induces proliferation of bone marrow leukocytes, the proportion of newly released leukocytes can be increased in inflammatory conditions in total leukocyte population. Such changes in leukocyte composition may likely influence LTL. Indeed, LTL depends to a large extent on the hematopoietic hierarchy. Only newly differentiated (naïve) leukocytes have telomere lengths similar to those of hematopoietic stem cell progenitors, while mature leukocytes have much shorter telomeres (Hastings et al., 2017; Kimura et al., 2010). If the considerations above are correct, then the interaction of multidirectional processes such as age-related telomere attrition, on the one hand, and chronic
The authors declare no competing interest. Author contributions DSK, MDK and AMV conceived the study design. VIK, VPC, OKK and VBS participated in data collection. DSK, MDK, VAK, VGG, VMK and AMV participated in data analysis and interpretation. VAK and VGG carried out the final statistical analysis. MDK and AMV drafted the manuscript and designed the figures. All authors approved the final version of the paper. Funding source The study was supported by a grant from the National Academy of Medical Sciences, Ukraine (Grant No. 0113U002166). The providers of the grant had no role in the conduct and design of research and in the interpretation of the data. Acknowledgements We are grateful to the doctors of family medicine, Natalia Lerman and Svetlana Yatsenko, and district endocrinologist, Yuri Pisarenko, for their helpful collaboration, as well as Maria Samusenko, Olena Holyk, Oksana Opanasenko and Svitlana Naskalova for technical assistance in conducting this study. We also thank Oksana Zabuga for the assistance in preparing the manuscript. References Adaikalakoteswari, A., Balasubramanyam, M., Ravikumar, R., Deepa, R., Mohan, V., 2007. Association of telomere shortening with impaired glucose tolerance and diabetic macroangiopathy. Atherosclerosis 195, 83–89. Arai, Y., Martin-Ruiz, C.M., Takayama, M., Abe, Y., Takebayashi, T., Koyasu, S., et al., 2015. Inflammation, but not telomere length, predicts successful ageing at extreme old age: a longitudinal study of semi-supercentenarians. EBioMedicine 2, 1549–1558. Aubert, G., Baerlocher, G.M., Vulto, I., Poon, S.S., Lansdorp, P.M., 2012. Collapse of telomere homeostasis in hematopoietic cells caused by heterozygous mutations in telomerase genes. PLoS Genet. 8, e1002696. Benjamin, D.J., Berger, J.O., Johannesson, M., Nosek, B.A., Wagenmakers, E.-J., Berk, R., et al., 2018. Redefine statistical significance. Nat. Hum. Behav. 2, 6–10. Berglund, K., Reynolds, C.A., Ploner, A., Gerritsen, L., Hovatta, I., Pedersen, N.L., et al., 2016. Longitudinal decline of leukocyte telomere length in old age and the association with sex and genetic risk. Aging (Albany NY) 8, 1398–1415. Blackburn, E.H., Epel, E.S., Lin, J., 2015. Human telomere biology: a contributory and
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interactive factor in aging disease risks and protection. Science 350, 1193–1198. Bonfigli, A.R., Spazzafumo, L., Prattichizzo, F., Bonafè, M., Mensà, E., Micolucci, L., et al., 2016. Leukocyte telomere length and mortality risk in patients with type 2 diabetes. Oncotarget 7, 50835–50844. Cawthon, R.M., 2009. Telomere length measurement by a novel monochrome multiplex quantitative PCR method. Nucleic Acids Res. 37, e21–e21. Chatterjee, S., Khunti, K., Davies, M.J., 2017. Type 2 diabetes. Lancet 389, 2239–2251. Chen, S., Yeh, F., Lin, J., Matsuguchi, T., Blackburn, E., Lee, E.T., et al., 2014. Short leukocyte telomere length is associated with obesity in American Indians: the Strong Heart Family study. Aging (Albany NY) 6, 380–389. D'Mello, M.J., Ross, S.A., Briel, M., Anand, S.S., Gerstein, H., Paré, G., 2015. Association between shortened leukocyte telomere length and cardiometabolic outcomes: systematic review and meta-analysis. Circ. Cardiovasc. Genet. 8, 82–90. Franceschi, C., Campisi, J., 2014. Chronic inflammation (inflammaging) and its potential contribution to age-associated diseases. J. Gerontol. A Biol. Sci. Med. Sci. 69 (Suppl 1), S4–S9. Frenck Jr., R.W., Blackburn, E.H., Shannon, K.M., 1998. The rate of telomere sequence loss in human leukocytes varies with age. Proc. Natl. Acad. Sci. U. S. A. 95, 5607–5610. Gizard, F., Heywood, E.B., Findeisen, H.M., Zhao, Y., Jones, K.L., Cudejko, C., et al., 2011. Telomerase activation in atherosclerosis and induction of telomerase reverse transcriptase expression by inflammatory stimuli in macrophages. Arterioscler. Thromb. Vasc. Biol. 31, 245–252. Greider, C.W., Blackburn, E.H., 1985. Identification of a specific telomere terminal transferase activity in Tetrahymena extracts. Cell 43, 405–413. Hastings, W.J., Shalev, I., Belsky, D.W., 2017. Translating measures of biological aging to test effectiveness of geroprotective interventions: what can we learn from research on telomeres? Front. Genet. 8, 164. Jaacks, L.M., Siegel, K.R., Gujral, U.P., Narayan, K.M., 2016. Type 2 diabetes: a 21st century epidemic. Best Pract. Res. Clin. Endocrinol. Metab. 30, 331–343. Khalangot, M., Gurianov, V., Okhrimenko, N., Luzanchuk, I., Kravchenko, V., 2016. Neck circumference as a risk factor of screen-detected diabetes mellitus: community-based study. Diabetol. Metab. Syndr. 8, 12. Khalangot, M.D., Kovtun, V.A., Okhrimenko, N.V., Gurianov, V.G., Kravchenko, V.I., 2017a. Glucose tolerance testing and anthropometric comparisons among rural residents of Kyiv region: investigating the possible effect of childhood starvation - a community-based study. Nutr. Metab. Insights 10 (1178638817741281). Khalangot, M., Krasnienkov, D., Vaiserman, A., Avilov, I., Kovtun, V., Okhrimenko, N., et al., 2017b. Leukocyte telomere length is inversely associated with post-load but not with fasting plasma glucose levels. Exp. Biol. Med. (Maywood) 242, 700–708. Khan, S.H., Masood, U., Hanif, M.S., Shah Bokhari, S.O.R., Khan, M.J., 2012. Effect of age and gender on blood lipids and glucose. Rec. Manag. J. 37, 344–347. Khan, S.S., Singer, B.D., Vaughan, D.E., 2017. Molecular and physiological manifestations and measurement of aging in humans. Aging Cell 16, 624–633. Kimura, M., Gazitt, Y., Cao, X., Zhao, X., Lansdorp, P.M., Aviv, A., 2010. Synchrony of telomere length among hematopoietic cells. Exp. Hematol. 38, 854–859. Koliada, A.K., Krasnenkov, D.S., Vaiserman, A.M., 2015. Telomeric aging: mitotic clock or stress indicator? Front. Genet. 6, 82. Ma, D., Yu, Y., Yu, X., Zhang, M., Yang, Y., 2015. The changes of leukocyte telomere length and telomerase activity after sitagliptin intervention in newly diagnosed type 2 diabetes. Diabetes Metab. Res. Rev. 31, 256–261. Menke, A., Casagrande, S., Cowie, C.C., 2015. Leukocyte telomere length and diabetes status, duration, and control: the 1999–2002 National Health and Nutrition
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Hyperglycemia attenuates the association between telomere length and age in Ukrainian population
T
Dmytro S. Krasnienkova, Mykola D. Khalangotb,c, Victor I. Kravchenkob, Volodymyr A. Kovtunb, Vitaly G. Guryanovd, Valentina P. Chizhovaa, Oleg V. Korkushkoa, Valery B. Shatiloa, ⁎ Vitaly M. Kukharskya, Alexander M. Vaisermana, a
Chebotariov Institute of Gerontology NAMS of Ukraine, Kyiv 04114, Ukraine Komisarenko Institute of Endocrinology and Metabolism NAMS of Ukraine, Kyiv 04114, Ukraine c Department of Endocrinology, Shupyk National Medical Academy of Postgraduate Education, Kyiv 04112, Ukraine d Department of Medical and Biological Physics and Informatics, Bogomolets National Medical University, Kyiv 02000, Ukraine b
A R T I C LE I N FO
A B S T R A C T
Section Editor: Holly M. Brown-Borg
Diabetes-related conditions such as chronic hyperglycemia and related oxidative stress and inflammation were repeatedly associated with accelerated telomere shortening in epidemiological studies, although some findings are inconsistent. In present study, we aimed to assess the impact of disturbances in glucose metabolism on association between age and leukocyte telomere length (LTL) in the Ukrainian population. The study was conducted on the 119 adult subjects aged between 43 and 87 years residing in the Kyiv region, Ukraine. LTL was determined by a quantitative PCR-based method. LTL was negatively correlated with the measure of abdominal obesity such as waist-hip ratio, as well as with both fasting plasma glucose (FPG) and two-hour post-load glucose (2hPG) levels. Consistently with previous studies, a significant negative association between LTL and age was observed in individuals with normal (< 5.6 mmol/L) FPG levels. Unexpectedly, however, no association was found in subjects with impaired glucose metabolism assessed by abnormal FPG or/and 2hPG levels. No association between LTL and age was observed in a logistic regression model; the association between LTL and age became significant after adjusting for FPG level. In the FPG-adjusted model, 1.6-time lower odds to have long telomere length were indicated for each 10 years increase in age. We hypothesize that the attenuation of association between LTL and age in hyperglycemic persons can likely be attributed to the interaction of multidirectional processes determining this relationship.
Keywords: Type 2 diabetes Hyperglycemia Inflammation Oxidative stress Aging Telomere length
1. Introduction Life expectancy has been significantly extended worldwide during the last century. Such increase of life expectancy is, however, not accompanied by corresponding improvement in health span as most present-day societies undergo rapid population aging (Vaiserman and Lushchak, 2017). Since aging is the main risk factor for most chronic disorders, the incidence of age-related pathologies including type 2 diabetes (T2D) rises to a large extent with increasing longevity. In recent decades, T2D is emerged as an epidemic worldwide. About 9% of the global adult population (around 415 million people in total) presently has diabetes; this number is expected to increase dramatically
and will reach 642 million people over the next decade (Chatterjee et al., 2017; Jaacks et al., 2016). T2D, accounting for > 90% of all diabetes cases, is commonly referred to as typical age-related disease. Its age-specific incidence and mortality rates rise exponentially with age, starting at age 40 and doubling with each successive 6–8-year period. As a result, about 20% of people over the age of 65 have T2D (Perry 3rd, 1999; Samos and Roos, 1998). The risk factors involved in the etiology of this disorder include genetic predisposition, unhealthy dietary habits, inadequate physical activity and stresses. Pathophysiological mechanisms of T2D include impaired β-cell function, peripheral insulin resistance and disturbed glucose metabolism (Skyler et al., 2017). Hyperglycemia is a common feature of T2D. One of the most
Abbreviations: 2hPG, two-hour post-load glucose; ADA, American Diabetes Association; AFG, abnormal fasting glucose; BMI, body mass index; DiastBP, diastolic blood pressure; FPG, fasting plasma glucose; IFG, impaired fasting glucose; IGT, impaired glucose tolerance; HC, hip circumference; LTL, leukocyte telomere length; NFG, normal fasting glucose; NGT, normal glucose tolerance; RTL, relative telomere length; SystBP, Systolic blood pressure; T2D, type 2 diabetes; TERT, telomerase reverse transcriptase; WC, waist circumference; WHO, World Health Organization; WHR, waist-hip ratio ⁎ Corresponding author at: Institute of Gerontology NAMS of Ukraine, Vyshgorodska str. 67, Kyiv 04114, Ukraine. E-mail address: [email protected] (A.M. Vaiserman). https://doi.org/10.1016/j.exger.2018.06.027 Received 23 January 2018; Received in revised form 23 June 2018; Accepted 25 June 2018 Available online 26 June 2018 0531-5565/ © 2018 Elsevier Inc. All rights reserved.
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details, see Shatylo et al., 2016]. 82 persons were recruited under the supervision of the Institute of Endocrinology and Metabolism from two local family medicine clinics of the Makariv rural district (Kyiv region) [for more sample details, see Khalangot et al. (2016, 2017a, b)]. The inclusion criteria were as follows: 1) middle-to-old age persons (minimal age: 43; maximum age: 87); 2) lack of previously diagnosed T2D. Thus, all participants involved in the study did not receive glucose-lowering drugs before the investigation started. This is an important point because treatment with such medications may likely influence LTLs, thereby biasing the results of the study [see, e.g., Ma et al., 2015]. The exclusion criteria were as follows: 1) residence in a region other than Kyiv; 2) inability to visit the clinic due to either chronic illness or disability; 3) refusal or inability to give informed consent.
important consequences of chronic hyperglycemia is oxidative stress leading, in turn, to chronic inflammation (Tangvarasittichai, 2015) and accelerated telomere shortening (Tamura et al., 2016a). Since all these processes are known to accompany aging, T2D is considered by several authors as a premature aging syndrome (Murillo-Ortiz et al., 2012). Telomeres are DNA-protein complexes that cap and protect the ends of eukaryotic chromosomes (Blackburn et al., 2015). The length of telomeres is regulated by a specific RNA-dependent DNA polymerase complex, telomerase, which catalyzes the addition of telomeric repeats to the ends of chromosomes to preserve their integrity. In most somatic cells, telomeric regions shorten during successive cell divisions (a process referred to as “telomere attrition”) due to insufficient telomerase activity. Therefore, cellular replication may go on until a critical threshold of telomere length is reached (Bonfigli et al., 2016). For this reason, the rate of telomere shortening is considered to be an indicator of replicative senescence, and telomere length, especially leukocyte telomere length (LTL), is widely used as a biomarker of organismal aging (Blackburn et al., 2015; Khan et al., 2017). Due to their chemical composition, telomeres are highly vulnerable to oxidative damage, and chronic oxidative stress, in particular that related to T2D and its complications, can accelerate telomeric shortening (Koliada et al., 2015). Based on the available data, it can be assumed that chronic hyperglycemia, oxidative stress, and telomere attrition in different tissues, including pancreatic beta cells and adipocytes, can be key components of a vicious cycle underlying the pathophysiology of T2D (Tamura et al., 2016a). The association of T2D and related conditions with short telomere length has been observed in many epidemiological studies. For example, LTLs have been shown to be significantly lower in pre-diabetic subjects with impaired glucose tolerance (IGT), lower still in T2D individuals without atherosclerotic plaques and lowest in T2D patients with atherosclerotic plaques compared to control subjects (Adaikalakoteswari et al., 2007). Shortened telomeres were also found in β-cells from autopsy pancreas obtained from T2D patients compared to age-matched control individuals (Tamura et al., 2014). In this study, telomere lengths were negatively correlated with glycated hemoglobin levels and telomeres were substantially shorter in T2D patients who had been treated with hypoglycemic medications than in those who had been not, indicating that an association exists between T2D severity and telomere attrition rate. A relationship between T2D and telomere length was evident in recent meta-analyses on this topic (D'Mello et al., 2015; Wang et al., 2016; Willeit et al., 2014; Zhao et al., 2013). This relationship was, however, significantly influenced by age, sex, body mass index (BMI), region of residence and diabetes type (Wang et al., 2016). Moreover, reports on the association of telomere length with T2D and associated cardiometabolic risk factors are conflicting in the literature (Zhao et al., 2013). For example, leukocyte telomere length has not been associated with T2D status and duration, as well as with poor glucose control in diabetic patients in the US general population (Menke et al., 2015). In our previous study, LTL was inversely associated with two-hour post-load glucose (2hPG) levels but not with fasting plasma glucose (FPG) levels (Khalangot et al., 2017a, b). Due to such inconsistency in the results, further studies need to be conducted for better understanding of causal relationships and pathways involved in this association. In present study, we aimed to assess the impact of disturbances in glucose metabolism on association between age and telomere length in Ukrainian population.
2.2. Ethical aspects The study protocol was approved by the Ethics Committees of the Institute of Endocrinology and Metabolism and Institute of Gerontology (both are part of the National Academy of Medical Sciences of Ukraine). All participants provided written informed consent. The Declaration of Helsinki (2000) and the applicable national standards as they relate to the involvement of human subjects in research were enforced. 2.3. Collection and storage of blood samples For the FPG test, blood was collected from all volunteers after a 10 h fast. For the oral glucose tolerance test, blood was collected for 2 h after ingestion of glucose (75 g of glucose per 200 mL of water) in the morning after at least 10 h of fasting. Blood was collected in EDTAcoated tubes and centrifuged at 1000g for 10 min. For DNA extraction, blood was collected in EDTA-coated tubes and stored at −80 °C until DNA extraction procedure. 2.4. Measurement of baseline characteristics BMI was determined as the body weight (in kg) divided by the height (in m) squared (kg/m2). Waist circumference (WC) was measured at the point of noticeable waist narrowing using a flexible anthropometric tape, with the subject in a standing position. Hip circumference (HC) was measured at the maximum circumference over the buttocks. The waist-hip ratio (WHR) was calculated by the ratio between WC and HC. Systolic blood pressure (SystBP) and diastolic blood pressure (DiastBP) (mm/Hg) were measured twice with a standard sphygmomanometer in a sitting position after at least 5 min of rest. Plasma glucose levels were determined by a standard glucose oxidase method. Among all subjects studied, 32 were categorized as normal fasting glucose (NFG) persons (FPG < 5.6 mmol/L), and 87 were categorized as abnormal fasting glucose (AFG) individuals (FPG ≥ 5.6 mmol/L), among them, 68 had impaired fasting glucose (IFG) levels (5.6–6.9 mmol/L, prediabetic persons) and 19 had diabetic (DIAB) FPG levels (> 6.9 mmol/L). According to the 2hPG test, 90 subjects had normal 2hPG levels (< 7.8 mmol/L; normal glucose tolerance, NGT), while 29 were categorized as abnormal glucose tolerance (AGT) persons (≥7.8 mmol/L), among them, 22 had prediabetic (IGT; 7.8–11.0 mmol/L) and 7 had diabetic (> 11.0 mmol/L) 2hPG levels. 2.5. Telomere length assay
2. Methods
The relative telomere lengths (RTLs) were measured by monochrome multiplex polymerase chain reaction in real time (qPCR) following the method described by Cawthon (2009). DNA was extracted from the whole blood by the phenol-chloroform purification method (Greider and Blackburn, 1985). PCR reaction mix was prepared using a commercial reagent kit for RT-PCR (Syntol, Russia) with addition of betaine (Sigma, USA) at a final concentration of 1 M. For multiplex
2.1. Participants 119 eligible participants were recruited for the study during the 2014 to 2016 timeframe. Among them, 37 persons were recruited from the clinic of Institute of Gerontology, Kyiv, Ukraine [for more sample 248
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shown). Such a trend change of the age-related dynamics of FPG in older age groups was observed in several other studies, e.g., in the study by Khan et al. (2012). This paradoxical change of the age-related trend can likely be explained by healthy lifestyle changes and/or by selection bias among elderly persons. T/S ratio was negatively correlated with a measure of abdominal obesity such as WHR. This result coincides with that obtained in American Indians (Chen et al., 2014). Expectedly, a significant negative correlation has been found between T/S ratio and both indicators of glucose metabolism (FPG and 2hPG) investigated. No relationship was found between LTL and age (R2 = 0.03, p = 0.07) (Fig. 1A). LTL was not associated with FPG levels in NFG subjects (R2 = 0.00002, p = 0.98) and was slightly associated in AFG subjects (R2 = 0.07, p = 0.02) (Fig. 1B). Slight association was found between LTL and 2hPG levels in NGT individuals (R2 = 0.055, p = 0.02); no such association was seen for AGT individuals (R2 = 0.12, p = 0.07) (Fig. 1C). T/S ratio was found to be dependent on FPG category (JT test: z = −2.25, p = 0.025), see also Fig. 2A. No significant association between T/S ratio and post-load (2hPG) categories was revealed (p > 0.05), while significant association trend was observed across different combinations of fasting and post-load glucose categories (JT test: z = −2.18, p = 0.029; Fig. 2B). Unexpectedly, no association of T/S ratio with age was observed in the sample studied (Table 2). We further examined this relationship in different FPG and 2hPG categories in order to investigate whether such an association exists in persons with normal and elevated blood glucose levels. Since two different cut-off values are used now to define the impaired fasting glycaemia state [World Health Organization (WHO): ≥ 6.1 mmol/L; American Diabetes Association (ADA): ≥ 5.6 mmol/L] (Yip et al., 2017), both these cut-off values were used in evaluating the results of the FPG test. A strongly significant correlation [even according to a higher criteria for statistical significance such as p < 0,005 recently proposed for reproducibility of research results (Benjamin et al., 2018)] between LTL (T/S ratio) and age was observed in subjects with normal FPG levels (Table 3); this association was weaker but still significant (Spearman r = −0.28, p = 0.02) if less strict cut-off (< 6.1 mmol/L) was used. No association of LTL with age was detected in persons with elevated FPG levels as well as with both normal and elevated 2hPG levels (Table 3). In studying relationship between T/S ratio and age in persons with normal glucose metabolism and with different combinations of abnormal fasting glucose and/or glucose tolerance, a highly significant correlation was found in the normal glucose (‘NFG AND NGT’) group, whereas no association was observed in both AFG or/and AGT groups (Table 4). Although a borderline significance only was observed for difference in median LTL values between NFG and AFG categories (Mann-Whitney test, p = 0.06), strongly differing relationship patterns between LTL and age were found within these categories. A clear difference in LTL levels was observed between different age groups in NFG persons; no such difference was found for AFG individuals (Fig. 2C).
qPCR, the telomere primer pair telg and telc (final concentrations 450 nM each) were combined with the albumin primer pair albu and albd (final concentrations 250 nM each) in the master mix. The thermal cycling profile was as follows: 15 min at 95 °C; 2 cycles of 15 s at 94 °C, 15 s at 49 °C; and 32 cycles of 15 s at 94 °C, 10 s at 62 °C, 15 s at 74 °C with signal acquisition, 10 s at 84 °C, and 15 s at 88 °C with signal acquisition. To obtain the calibration curve, PCR was carried out at four concentrations of the reference DNA in duplicates which cover a range of 27-fold dilutions, prepared by serial dilution. All DNA samples were run in triplicate. Amplification curves were generated by the Opticon Monitor 3 software. After the thermal cycling and raw data collection was completed, the Opticon Monitor 3 software was used to generate two standard curves for each plate, one for the telomere signal and another for the single-copy gene (scg) albumin signal. The telomere length was expressed as the T/S ratio, the telomere repeat copy number (T) to the scg copy number (S). 2.6. Statistical analysis The Shapiro-Wilk test was used to check the normality of variable distributions. Both parametric and non-parametric tests were used for statistical analyses. The parametric Pearson's or non-parametric Spearman's correlation tests were used depending on the normality of the data to investigate relationships between variables. Pairwise comparisons between means were performed using Student's t-test and comparisons between medians were performed using Mann–Whitney U test. The significance of changes in LTL (T/S ratio) over glucose categories or over age groups was tested by the Jonckheere-Terpstra (JT) trend test. Multivariate logistic regression models were used to estimate odds ratios (OR) and 95% confidence intervals (CIs) for the association between T/S ratio (dependent variable) and age (independent variable), accounting for potential confounders. Analyses were performed by Statistica 8.0 (StatSoft Inc., USA) and Medcalc v. 18 (MedCalc Software, Belgium) softwares. 3. Results The demographic, anthropometric and clinical characteristics of the studied population are presented in the Table 1. As we can see from the table, the anthropometric parameters BMI, WC and WHR were normally distributed (marked in bold in the table), allowing parametric statistical tests to be performed. For the parameters BMI, HC and WHR, statistically significant differences between sexes have been obtained; those differences were absent for other parameters studied including the relative LTL (T/S ratio). Correlations among parameters investigated are presented in Table 2. Surprisingly, FPG levels were negatively correlated with age. The direction of this association was negative in the total sample studied because FPG levels were positively related to age up to about 60 years of age and then steadily declined across age groups (data not Table 1 Baseline characteristics of the studied population. Parameter
All (n = 119)
Male (n = 35)
Female (n = 84)
p (M vs. F)
Age, yrs SystBP, mm Hg DiastBP, mm Hg FPG, mmol/L 2hPG, mmol/L LTL, T/S ratio HC, cm BMI, kg/m2 WC, cm WHR
62.0 (57.0–68.0) 135 (125–155) 84 (80–95) 6.0 (5.5–6.7) 6.4 (5.1–7.6) 0.61 (0.50–0.72) 110 (105–122) 31.91 ( ± 6.37) 102.3 ( ± 14.4) 0.93 ( ± 0.08)
60.0 (54.0–66.8) 126 (122–147) 82 (80–92) 6.0 (5.3–6.6) 6.1 (5.0–8.6) 0.61 (0.49–0.79) 101 (98–109) 29.44 ( ± 3.85) 101.1 ( ± 8.9) 0.97 ( ± 0.06)
62.0 (57.0–69.5) 140 (126–155) 84 (77–95) 6.0 (5.5–6.7) 6.5 (5.1–7.6) 0.61 (0.50–0.71) 110 (105–122) 32.9 ( ± 6.91) 102.8 ( ± 16.1) 0.91 ( ± 0.08)
0.21a 0.06a 0.88a 0.75a 0.72a 0.35a < 0.001a 0.007b 0.48b < 0.001b
Notes: Normally distributed characteristics (marked in bold) are expressed as mean ± standard deviation (SD) values. Non-normally distributed characteristics are expressed as medians (Q1–Q3). Medians are compared by the Mann–Whitney U testa and means are compared by the Student t-testb. 249
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Table 2 Correlation matrix between measured variables.
T/S Age WC HC WHR BMI FPG 2hPG SistBP DiastBP
T/S
Age
WC
HC
WHR
BMI
FPG
2hPG
SistBP
DiastBP
1 – – – −0.22 – −0.24 −0.24 – –
1 – – 0.20 – −0.24 – 0.32 –
1 0.79 0.58⁎ 0.85⁎ 0.40 – 0.29 –
1 – – 0.33 – – –
1 0.28⁎ 0.23 – – –
1 0.39 – 0.21 –
1 0.24 – –
1 – –
1 0.58
1
Notes: Correlations are determined by Spearman or Pearson tests depending on the normality of the data. Statistically significant correlations only (p < 0.05) are shown. Significant Pearson correlation coefficients are marked with asterisks. Non-significant correlations are marked with dashes.
Fig. 1. Relationship plots for various fasting glucose and post-load glucose categories. (A) Gross profile of relationship between LTL (T/S ratio) and age (n = 119). (B) Relationship plots for various fasting glucose categories: NFG: normal fasting glucose (n = 32); AFG: abnormal fasting glucose (n = 87); (C) Relationship plots for various 2hPG categories: NGT: normal glucose tolerance (n = 90); AGT: abnormal glucose tolerance (n = 29).
Fig. 2. Box-and-whisker plots for various fasting or post-load glucose categories and for different age groups. (A) Individual data points and box-and-whisker plots for various fasting glucose categories. (B) Individual data points and box-and-whisker plots for various combinations of fasting and post-load glucose categories. (C) Individual data points and box-and-whisker plots for different age groups. In each box-and-whisker plot, the box represents the values from the lower to upper quartile (25 to 75 percentile). The middle line inside the box represents the median. The whiskers above and below the box show the locations of the minimum and maximum values. Data points outside of boxplot whiskers are outliers, defined as being beyond 1.5 interquartile range of each quartile. NFG: normal fasting glucose (n = 32); AFG: abnormal fasting glucose (n = 87); IFG: impaired fasting glucose (n = 68); DIAB – diabetes (n = 19); NGT: normal glucose tolerance (n = 90); AGT: abnormal glucose tolerance (n = 29). NFG AND NGT: n = 23; AFG OR AGT: n = 76; AFG AND AGT: n = 20. Age categories: NFG: 40–59, n = 6; 60–79, n = 19; ≥80, n = 7; AFG: 40–59, n = 39; 60–79, n = 45; ≥80, n = 3.
The impact of potential confounders on the association between T/S ratio and age was evaluated by multivariate logistic regression model. In a univariate (unadjusted) model, no association between T/S ratio and age was observed (Table 5). After stepwise selection of covariates, only age remained significant predictor and was included in the final adjusted model. After adjusting for FPG level, the association between T/S ratio and age became significant. In the FPG-adjusted model, the odds to have long telomere length (defined as being in the highest tertile of FPG) decreased significantly with age. In this model, 1.6-time lower odds to have long telomere length were indicated for each 10 years increase in age.
Table 3 Spearman rank-order correlations between LTL and age in persons with normal and elevated fasting blood glucose levels. Type of blood glucose test
Cut-off value, mmol/L
N
Spearman r
p-Level
FPG
< 5,6 ≥5,6 < 7,8 ≥7,8
32 87 90 29
−0.55 −0.05 −0.05 −0.18
0.001 0.65 0.61 0.33
2hPG
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inflammation-related telomerase activation in inflammatory cells and changes in leukocyte composition, on the other hand, would bias the association between telomere length and age. This can be especially important in older age groups commonly characterized by a chronic, low-grade inflammation (“inflammaging”), which is an important risk factor for most if not all age-related disorders (Franceschi and Campisi, 2014). It may, in particular, provide a potential explanation for the decline in the rate of telomere attrition with age (Frenck Jr et al., 1998; Zeichner et al., 1999; Aubert et al., 2012; Tamura et al., 2016b), as well as for a paradoxical age-related telomere elongation observed in several older age groups in both cross-sectional and longitudinal studies (Arai et al., 2015; Berglund et al., 2016). In view of these considerations, we plan to investigate in our further research whether both inflammationinduced telomerase activation and altered leukocyte composition can affect the association between LTL and age. The limitation of our research is that cross-sectional design used in the study precludes any strong conclusions on causality. Therefore, longitudinal observation should be conducted further to verify our findings. Finally, it should be noted that the sample size of present study is too small to draw definitive conclusions. Therefore, the reported findings should be regarded as preliminary only and need further evaluation in larger samples.
Table 4 Spearman rank-order correlations between LTL and age in persons with normal glucose metabolism and with abnormal fasting glucose and/or glucose tolerance levels. Category
Results of glucose tests, mmol/L
N
Spearman r
p-Level
‘NFG AND NGT’ group ‘AFG OR AGT’ group ‘AFG AND AGT’ group
FPG < 5.6; 2hPG < 7.8
23
−0.56
0.01
FPG ≥ 5.6 OR 2hPG ≥ 7.8 FPG ≥ 5.6; 2hPG ≥ 7.8
76 20
−0.10 0.15
0.43 0.54
Table 5 Unadjusted and adjusted logistic regression model estimates of association between T/S ratio and age. Variable a
Age (unit = 10 years) Age (unit = 10 years)b
β coefficient ± S.E.
OR
95% CI
p
0.32 ± 0.20 0.44 ± 0.21
1.37 1.56
0.92–2.04 1.02–2.38
0.12 0.04
Notes: aunivariate model; bFPG-adjusted (FPG unit = 1 mmol/L) model.
4. Discussion Conflict of interests
To our knowledge, our study is the first to examine the association of LTL with age in persons with normal and abnormal glucose metabolism. Consistently with previous studies, negative association between LTL and age was observed in individuals with normal FPG levels. Unexpectedly, however, no association was found in subjects with impaired glucose metabolism assessed by subnormal FPG levels. We hypothesized that such attenuation of relationship can be attributed to superposition of opposite effects determining this association. On the one hand, hyperglycemia-related oxidative stress and related inflammatory processes can accelerate age-related telomere attrition (Koliada et al., 2015). For example, a higher systemic inflammatory load was shown to be associated with short LTL in an older population (O'Donovan et al., 2011). On the other hand, inflammation can likely induce telomerase activity thereby causing the telomere elongation. For example, telomerase was shown to be transiently expressed in human T lymphocytes upon activation (Roth et al., 2003). Supporting evidence comes from the study conducted in persons with metabolic syndrome, a group of metabolic risk factors such as IGT, insulin resistance, dyslipidemia, elevated blood pressure and obesity (Rentoukas et al., 2012). In this research, an increased telomerase activity was observed in circulating peripheral blood mononuclear cells, along with elevated markers of inflammation and endothelial dysfunction in these persons. The enhancement of telomerase activity was also found in macrophages by the inflammatory remodeling mediating atherosclerosis formation in the low-density lipoprotein receptor-deficient mice model (Gizard et al., 2011). In this study, inflammatory stimuli resulted in telomerase activation in macrophages by inducing the expression of the catalytic subunit telomerase reverse transcriptase (TERT). Restricted glucose conditions, on the contrary, were found to be associated with a significant decrease in the telomerase activity associated with a significant reduction in the TERT expression in breast cancer cells (Wardi et al., 2014). An important point is that, since chronic inflammation induces proliferation of bone marrow leukocytes, the proportion of newly released leukocytes can be increased in inflammatory conditions in total leukocyte population. Such changes in leukocyte composition may likely influence LTL. Indeed, LTL depends to a large extent on the hematopoietic hierarchy. Only newly differentiated (naïve) leukocytes have telomere lengths similar to those of hematopoietic stem cell progenitors, while mature leukocytes have much shorter telomeres (Hastings et al., 2017; Kimura et al., 2010). If the considerations above are correct, then the interaction of multidirectional processes such as age-related telomere attrition, on the one hand, and chronic
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