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Aging and disease    2018, Vol. 9 Issue (1) : 77-89     DOI: 10.14336/AD.2017.0310
Orginal Article |
Short Telomere Length is Associated with Aging, Central Obesity, Poor Sleep and Hypertension in Lebanese Individuals
Zgheib Nathalie K1, Sleiman Fatima1, Nasreddine Lara2, Nasrallah Mona3, Nakhoul Nancy3, Isma’eel Hussain3, Tamim Hani3,4,*
1Department of Pharmacology & Toxicology, Faculty of Medicine, American University of Beirut, Lebanon
2Department of Nutrition & Food Sciences, Faculty of Agriculture and Food Sciences, American University of Beirut, Lebanon
3Department of Internal Medicine, Faculty of Medicine, American University of Beirut Medical Center, Lebanon
4Clinical Research Institute, Faculty of Medicine, American University of Beirut Medical Center, Lebanon
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Abstract  

In Lebanon, data stemming from national cross-sectional surveys indicated significant increasing trends in the prevalence of cardiovascular diseases and associated behavioral and age-related risk factors. To our knowledge, no data are available on relative telomere length (RTL) as a potential biomarker for age-related diseases in a Lebanese population. The aim of this study was to evaluate whether there is an association between RTL and demographic characteristics, lifestyle habits and diseases in the Lebanese. This was a cross-sectional study of 497 Lebanese subjects. Peripheral blood RTL was measured by amplifying telomere and single copy gene using real-time PCR. Mean ± SD RTL was 1.42 ± 0.83, and it was categorized into 3 tertiles. Older age (P=0.002) and wider waist circumference (WC) (P=0.001) were statistically significantly associated with shorter RTL. Multinomial logistic regression showed that subjects who had some level of sleeping difficulty had a statistically significantly shorter RTL when compared to those with no sleeping difficulties at all [OR (95% CI): 2.01 (1.11-3.62) in the first RTL tertile]. Importantly, statistically significantly shorter RTL was found with every additional 10 cm of WC [OR (95% CI): 1.30 (1.11-1.52) for first RTL tertile]. In addition, and after performing the multivariate logistic regression and adjusting for “predictors” of RTL, the odds of having hypertension or being treated for hypertension were higher in patients who had shorter RTL: OR (95% CI): 2.45 (1.36-4.44) and 2.28 (1.22-4.26) in the first RTL tertiles respectively with a similar trend, though not statistically significant, in the second RTL tertiles. This is the first study in Lebanon to show an association between age, central obesity, poor sleep and hypertension and RTL. It is hoped that telomere length measurement be potentially used as a biomarker for biological age and age-related diseases and progression in the Lebanese.

Keywords Aging      Hypertension      Obesity      Relative telomere length      Sleep     
Corresponding Authors: Tamim Hani   
About author:

These authors contributed equally to this work.

Issue Date: 01 February 2018
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Zgheib Nathalie K
Sleiman Fatima
Nasreddine Lara
Nasrallah Mona
Nakhoul Nancy
Isma’eel Hussain
Tamim Hani
Cite this article:   
Zgheib Nathalie K,Sleiman Fatima,Nasreddine Lara, et al. Short Telomere Length is Associated with Aging, Central Obesity, Poor Sleep and Hypertension in Lebanese Individuals[J]. Aging and disease, 2018, 9(1): 77-89.
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http://www.aginganddisease.org/EN/10.14336/AD.2017.0310     OR     http://www.aginganddisease.org/EN/Y2018/V9/I1/77
RTL
<1.06
(n=166)
1.06 - 1.432
(n=165)
>1.432
(n=166)
p-value
Age (years)Mean (±SD)48.56 ± 14.7544.70 ± 15.0142.92 ± 15.030.002
<4043 (25.9)161 (37.0)75 (45.2)0.009
40-6091 (54.8)78 (47.3)67 (40.42)
>6032 (19.3)26 (15.8)24 (14.5)
GenderFemale105 (63.3)106 (64.2)108 (65.1)0.94
Marital statusMarried106 (63.9)114 (69.1)109 (65.7)0.17
Single28 (16.9)33 (20.0)37 (22.3)
Other232 (19.3)18 (10.9)20 (12.0)
Income<600$58 (35.4)48 (29.4)46 (27.7)0.31
600-999.9$58 (35.4)55 (33.7)56 (33.7)
1000 $35 (21.3)41 (25.2)52 (31.3)
I don’t know/no answer13 (7.9)19 (11.7)12 (7.2)
EducationNo schooling or primary school74 (44.6)55 (33.3)52 (31.7)0.03
Intermediate school30 (18.1)56 (33.9)48 (29.3)
Secondary school or technical diploma43 (25.9)39 (23.6)44 (26.8)
University degree19 (11.4)15 (9.1)20 (12.2)
Crowding indexMean (±SD)1.52 ± 0.841.55 ± 0.901.50 ± 0.910.86
Current smoker104 (62.7)112 (67.9)103 (62.0)0.48
Current cigarette smoker74 (44.6)77 (46.7)63 (38.0)0.25
Current narghileh smoker46 (27.7)44 (26.7)51 (30.7)0.70
Current Alcohol Drinker34 (20.5)35 (21.2)26(15.7)0.38
Coffee Drinker133 (80.1)136 (82.4)130 (78.3)0.64
BMI (kg/m2)Mean (±SD)29.96 ± 5.7328.92 ± 5.6528.40 ± 5.940.045
BMI (kg/m2)- categorical3076 (45.8)69 (41.8)62 (37.3)0.30
Waist circumference (cm)Mean (±SD)98.91 ± 14.4695.87 ± 17.5292.75 ± 14.120.001
Body fat (Kg)Mean (±SD)30.29 ± 11.2828.24 ± 11.0927.30 ± 12.060.05
Muscle mass (Kg)Mean (±SD)26.45 ± 6.8026.24 ± 6.2426.19 ± 6.190.93
Levels of physical activityLow-intensity activity76 (45.8)82 (49.7)80 (48.2)0.97
Moderate-intensity activity54 (32.5)49 (29.7)51 (30.7)
High-intensity activity36 (21.7)34 (20.6)35 (21.1)
Physical activityNone29 (17.5)26 (15.8)24 (14.5)0.75
Any137 (82.5)139 (84.2)142 (85.5)
Number of hours sleep per night on weekdays4 hours26 (15.7)18 (10.9)23 (13.9)0.26
5-6 hours43 (25.9)47 (28.5)40 (24.1)
6-7 hours50 (30.1)37 (22.4)44 (26.5)
7-8 hours21 (12.7)37 (22.4)34 (20.5)
8-9 hours16 (9.6)13 (7.9)19 (11.4)
9 hours10 (6.0)13 (7.9)6 (3.6)
Number of hours sleep per night on weekend4 hours23 (13.9)20 (12.1)21 (12.7)0.80
5-6 hours30 (18.1)32 (19.4)30 (18.1)
6-7 hours37 (22.3)31 (18.8)31 (18.7)
7-8 hours26 (15.7)35 (21.2)37 (22.3)
8-9 hours20 (12.0)23 (13.9)27 (16.3)
9 hours30 (18.1)24 (14.5)20 (12.0)
Feel that you are not getting enough sleepNever61 (37.4)60 (37.0)57 (35.2)0.83
Rarely/sometimes/Frequently48 (29.4)40 (24.7)46 (28.4)
Almost always54 (33.1)62 (38.3)59 (36.4)
Sleep difficultiesNever32 (19.3)40 (24.2)50 (30.1)0.03
Rarely/sometimes/Frequently63 (38.0)42 (25.5)50 (30.1)
Almost always71 (42.8)83 (50.3)66 (39.8)
Table 1  The association of RTL with baseline characteristics and lifestyle.
Figure 1.  Correlation of relative telomere length with age and waist circumference

Scatter plots showing the correlation of age (A) and waist circumference (B) with the relative telomere length of peripheral leucocyte blood respectively in males and females. The grey line and (×) represent the males (n=178) and the black line and (o) represent the females (n=319). The P-values were calculated from the linear regression analyses of the relationships between age, waist circumference and RTL in males and females, and the Pearson correlation was used.

RTL
<1.06
(n=166)
1.06 - 1.432
(n=165)
>1.432
(n=166)
p-value
Diabetes
Definite diabetes225 (15.1)130 (18.2)20 (12.0)0.30
Self-reported diabetes or hyperglycemia diagnosis24 (14.5)27 (16.4)13 (7.8)0.05
Diabetes treatment26 (15.7)23 (13.9)12 (7.2)0.05
Fasting blood sugar (mg/dL)Abnormal90 (54.2)81 (49.1)76 (45.8)0.30
Insulin (µIU/mL)Mean (±SD)28.28 ± 11.1029.68 ± 18.5928.29 ± 19.460.74
HbA1C (%)Mean (±SD)5.97 ± 1.386.02 ± 1.385.80 ± 1.300.32
C peptide (ng/dL)Mean (±SD)3.34 ± 1.513.01 ± 1.283.01 ± 1.590.07
Hypertension (HTN)
Definite HTN373 (44.2)61 (37.0)47 (28.3)0.01
Self-reported HTN diagnosis56 (33.7)39 (23.6)23 (13.9)<0.0001
HTN treatment52 (31.3)36 (21.8)22 (13.3)<0.0001
Systolic blood pressure (mm/Hg)Mean (±SD)122.38 ± 17.82123.01 ± 21.22119.41 ± 18.340.19
Diastolic blood pressure (mm/Hg)Mean (±SD)75.38 ± 9.3174.95 ± 10.8173.94 ± 9.760.40
Dyslipidemia
Self-reported dyslipidemia diagnosis53 (31.9)35 (21.2)31 (18.7)0.01
Dyslipidemia treatment43 (25.9)28 (17.0)23 (13.9)0.02
HDL (mg/dL)Mean (±SD)50.04 ± 15.0249.47 ± 14.2949.45 ± 15.220.92
LDL (mg/dL)Mean (±SD)110.64 ± 35.45111.65 ± 42.53104.08 ± 34.340.14
Triglycerides (mg/dL)Mean (±SD)142.23 ± 70.39151.04 ± 141.95131.12 ± 79.070.21
Metabolic Syndrome
Metabolic Syndrome490 (54.2)79 (47.9)85 (51.2)0.51
Atherosclerotic cardiovascular disease (ASCVD) 10yrs Risk
ASCVD 10yrs Risk (%)5Mean (±SD)7.82 ± 11.727.82 ± 14.274.94 ± 7.030.04
Others
CRP (mg/L)Mean (±SD)13.07 ± 8.8013.01 ± 13.3510.28 ± 6.890.02
Cortisol (µg/dL)Mean (±SD)18.35 ± 10.4319.14 ± 13.9516.89 ± 10.590.22
Table 2  The association of RTL with chronic diseases and laboratory measurements.
RTL
≤1.0601.060 - 1.432>1.432
VariablesOR ( 95 % CI )P-valueOR ( 95 % CI )P-value
Education - Intermediate school0.50 (0.28 - 0.89)0.021.22 (0.71 - 2.11)0.47Reference
Education - Secondary school/technical diploma0.84 (0.47 - 1.48)0.551.02 (0.57 - 1.83)0.95Reference
Education - University degree0.98 (0.45 - 2.11)0.960.99 (0.44 - 2.23)0.99Reference
Waist circumference -WC (cm)1.30 (1.11 - 1.52)0.0011.17 (1.00 - 1.37)0.05Reference
Any Sleeping Difficulty - Rarely, sometimes, or frequently2.01 (1.11 - 3.62)0.021.04 (0.58 - 1.88)0.88Reference
Any Sleeping Difficulty - Almost always1.73 (0.97 - 3.08)0.061.55 (0.91 - 2.67)0.11Reference
Table 3  Stepwise multinomial logistic regression of potentially significant predictors of RTL including waist circumference as a marker for obesity with RTL
≤1.060
(n=166)
P-valueRTL
1.060 - 1.432
(n=165)
P-value>1.432
(n=166)
Diabetes
Definite diabetes
Cases, n (%)25 (15.1)30 (18.2)20 (12.0)
Unadjusted1.29 (0.69 - 2.43)0.421.62 (0.88 - 2.99)0.12Reference
Adjusted0.76 (0.38 - 1.52)0.431.29 (0.65 - 2.56)0.46Reference

Diabetes diagnosis
Cases, n (%)24 (14.5)27 (16.4)13 (7.8)
Unadjusted1.99 (0.97 - 4.06)0.062.30 (1.14 - 4.64)0.02Reference
Adjusted1.31 (0.61 - 2.81)0.481.94 (0.91 - 4.13)0.09Reference

Diabetes treatment
Cases, n (%)26 (15.7)23 (13.9)12 (7.2)
Unadjusted2.38 (1.16 - 4.90)0.022.08 (1.00 - 4.33)0.05Reference
Adjusted1.67 (0.78 - 3.56)0.191.71 (0.79 - 3.73)0.17Reference

Abnormal fasting blood sugar
Cases, n (%)90 (54.2)81 (49.1)76 (45.8)
Unadjusted1.40 (0.91 - 2.16)0.121.14 (0.74 - 1.76)0.55Reference
Adjusted0.92 (0.57 - 1.50)0.740.96 (0.59 - 1.56)0.88Reference

Insulin (µIU/mL)
Mean, SD28.28 ± 11.1029.68 ± 18.5928.29 ± 19.46
Unadjusted-0.01 (-4.03; 4.02)1.001.40 (-2.66; 5.46)0.50Reference
Adjusted-2.34 (-6.20; 1.52)0.23-0.06 (-3.92; 3.81)0.98Reference

HbA1C (%)

Mean, SD5.97 ± 1.386.02 ± 1.385.80 ± 1.30
Unadjusted0.16 (-0.13; 0.45)0.290.22 (-0.08; 0.51)0.15Reference
Adjusted-0.10 (-0.37; 0.17)0.480.09 (-0.18; 0.36)0.50Reference

C-peptide (ng/dL)
Mean, SD3.34 ± 1.513.01 ± 1.283.01 ± 1.59
Unadjusted0.33 (0.0; 0.64)0.040.002 (-0.32; 0.32)0.99Reference
Adjusted0.09 (-0.20; 0.38)0.55-0.12 (-0.41; 0.17)0.41Reference
Hypertension (HTN)
Definite HTN
Cases, n (%)73 (44.2)61 (37.0)47 (28.3)
Unadjusted2.01 (1.27 - 3.17)0.0031.48 (0.93 - 2.36)0.09Reference
Adjusted1.42 (0.85 - 2.38)0.181.31 (0.77 - 2.23)0.31Reference

HTN diagnosis
Cases, n (%)56 (33.7)39 (23.6)23 (13.9)
Unadjusted3.16 (1.83 - 5.46)<0.00011.92 (1.09 - 3.40)0.02Reference
Adjusted2.45 (1.36 - 4.44)0.0031.71 (0.92 - 3.19)0.09Reference

HTN treatment
Cases, n (%)52 (31.3)36 (21.8)22 (13.3)
Unadjusted2.99 (1.71 - 5.20)<0.00011.83 (1.02 - 3.27)0.04Reference
Adjusted2.28 (1.22 - 4.26)0.011.61 (0.83 - 3.11)0.15Reference

Systolic blood pressure (mm/Hg)
Mean, SD122.38 ± 17.82123.01 ± 21.22119.41 ± 18.34
Unadjusted2.97 (-1.18; 7.12)0.163.60 (-0.54; 7.75)0.09Reference
Adjusted-0.20 (-3.95; 3.56)0.252.17 (-1.54; 5.88)0.25Reference

Diastolic blood pressure (mm/Hg)
Mean, SD75.38 ± 9.3174.95 ± 10.8173.94 ± 9.76
Unadjusted1.44 (-0.72; 3.60)0.191.01 (-1.15; 3.17)0.36Reference
Adjusted-0.20 (-2.21; 1.82)0.850.28 (-1.71; 2.27)0.78Reference

Dyslipidemia
Dyslipidemia diagnosis
Cases, n (%)53 (31.9)35 (21.2)31 (18.7)
Unadjusted2.04 (1.23 - 3.40)0.011.17 (0.68 - 2.01)0.56Reference
Adjusted1.42 (0.82 - 2.47)0.210.94 (0.52 - 1.70)0.84Reference

Dyslipidemia treatment
Cases, n (%)43 (25.9)28 (17.0)23 (13.9)
Unadjusted2.17 (1.24 - 3.81)0.011.27 (0.70 - 2.31)0.43Reference
Adjusted1.59 (0.87 - 2.91)0.131.03 (0.54 - 1.96)0.92Reference

HDL (mg/dL)
Mean, SD50.04 ± 15.0249.47 ± 14.2949.45 ± 15.22
Unadjusted0.59 (-2.61 ; 3.79)0.720.02 (-3.19 ; 3.23)0.99Reference
Adjusted2.05 (-1.10 ; 5.19)0.200.76 (-2.36 ; 3.88)0.63Reference

LDL (mg/dL)
Mean, SD110.64 ± 35.45111.65 ± 42.53104.08 ± 34.34
Unadjusted6.56 (-1.55 ; 14.67)0.117.57 (-0.56 ; 15.70)0.07Reference
Adjusted2.65 (-5.40 ; 10.70)0.526.56 (-1.40 ; 14.53)0.11Reference

Triglycerides (mg/dL)
Mean, SD142.23 ± 70.39151.04 ± 141.95131.12 ± 79.07
Unadjusted11.11 (-10.92 ; 33.14)0.3219.92 (-2.14 ; 41.98)0.08Reference
Adjusted3.68 (-17.93 ; 25.29)0.7413.42 (-7.94 ; 34.78)0.22Reference

Metabolic syndrome2
Cases, n (%)90 (54.2)79 (47.9)85 (51.2)
Unadjusted1.13 (0.73 - 1.74)0.580.87 (0.57 - 1.35)0.54Reference
Adjusted0.87 (0.55 - 1.39)0.570.75 (0.47 - 1.19)0.22Reference

Atherosclerotic cardiovascular disease (ASCVD) 10yrs Risk3
Mean, SD7.82 ± 11.727.82 ± 14.274.94 ± 7.03
Unadjusted2.88 (0.34 ; 5.42)0.032.88 (0.36 ; 5.41)0.02Reference
Adjusted1.54 (-0.95 ; 4.04)0.222.39 (-0.05 ; 4.82)0.05Reference

Other

CRP (mg/L)
Mean, SD13.07 ± 8.8013.01 ± 13.3510.28 ± 6.89
Unadjusted2.78 (0.62 ; 4.95)0.012.73 (0.56 ; 4.90)0.01Reference
Adjusted1.78 (-0.33 ; 3.89)0.102.38 (0.28 ; 4.49)0.03Reference

Cortisol (µg/dL)
Mean, SD18.35 ± 10.4319.14 ± 13.9516.89 ± 10.59
Unadjusted1.46 (-1.10 ; 4.03)0.262.25 (-0.34 ; 4.84)0.09Reference
Adjusted1.94 (-0.62 ; 4.51)0.142.53 (-0.04 ; 5.11)0.05Reference
Table 4  Stepwise multivariate logistic regression of RTL tertiles with chronic disease and laboratory values with and without adjustment with potentially significant predictors of RTL1
RTL
≤1.0601.060 - 1.432>1.432
VariablesOR (95 % CI )P-valueOR (95 % CI)P-value
Age - 40-60 years2.41 (1.45 - 4.02)0.0011.35 (0.84 - 2.19)0.22Reference
Age - >60 years2.12 (1.06 - 4.23)0.031.25 (0.63 - 2.48)0.52Reference
Education - Intermediate school0.49 (0.27 - 0.88)0.021.19 (0.68 - 2.06)0.54Reference
Education - Secondary school or technical diploma0.88 (0.49 - 1.58)0.671.00 (0.55 - 1.82)1.00Reference
Education - University degree0.96 (0.45 - 2.06)0.910.90 (0.40 - 2.01)0.80Reference
Any sleeping difficulty - Rarely, sometimes, or frequently2.04 (1.13 - 3.69)0.021.04 (0.58 - 1.88)0.89Reference
Any sleeping difficulty - Almost always1.61 (0.90 - 2.86)0.111.49 (0.87 - 2.55)0.15Reference
Supplementary Table 1  Stepwise multinomial logistic regression of potentially significant predictors of RTL including body mass index as a marker for obesity with RTL
[1] Samassekou O, Gadji M, Drouin R+, Yan J (2010). Sizing the ends: Normal length of human telomeres. Ann Anat, 192(5):284-291.
[2] Palm W, de Lange T (2008). How Shelterin Protects Mammalian Telomeres. Annu Rev Genet, 42(1):301-334.
[3] Blackburn EH (1991). Structure and function of telomeres. Nature, 350(6319):569-573.
[4] Mu J, Wei LX (2002). Telomere and telomerase in oncology. Cell Res, 12(1):1-7.
[5] Cong YS, Wright WE, Shay JW (2002). Human Telomerase and Its Regulation. Microbiol Mol Biol Rev, 66(3):407-425.
[6] Shay JW, Wright WE (2011). Role of telomeres and telomerase in cancer. Semin Cancer Biol, 21(6):349-353.
[7] Takubo K, Izumiyama-Shimomura N, Honma N, Sawabe M, Arai T, Kato M,et al (2002). Telomere lengths are characteristic in each human individual. Exp Gerontol, 37(4):523-531.
[8] Cawthon RM, Smith KR, O’Brien E, Sivatchenko A, Kerber RA (2003). Association between telomere length in blood and mortality in people aged 60 years or older. Lancet, 361(9355):393-593.
[9] Yang Z, Huang X, Jiang H, Zhang Y, Liu H, Qin C,et al (2009). Short Telomeres and Prognosis of Hypertension in a Chinese Population. Hypertension, 53(4):639-645.
[10] Epel ES, Merkin SS, Cawthon R, Blackburn EH, Adler NE, Pletcher MJ,et al (2009). The rate of leukocyte telomere shortening predicts mortality from cardiovascular disease in elderly men. Aging (Albany NY), 1(1):81-88.
[11] Van der Harst P, van der Steege G, de Boer RA, Voors AA, Hall AS, Mulder MJ,et al (2007). Telomere length of circulating leukocytes is decreased in patients with chronic heart failure. J Am Coll Cardiol, 49(13):1459-1464.
[12] Brouilette S, Singh RK, Thompson JR, Goodall AH, Samani NJ (2003). White cell telomere length and risk of premature myocardial infarction. Arterioscler Thromb Vasc Biol, 23(5):842-846.
[13] Dudinskaya EN, Tkacheva ON, Shestakova MV, Brailova NV, Strazhesko ID, Akasheva DU,et al (2015). Short telomere length is associated with arterial aging in patients with type 2 diabetes mellitus. Endocr Connect, 4(3):136-143.
[14] Bekaert S, De MT, Van OP (2005). Telomere attrition as ageing biomarker. Anticancer Res, 25(4):3011-3021.
[15] Slagboom PE, Droog S, Boomsma DI (1994). Genetic determination of telomere size in humans: a twin study of three age groups. Am J Hum Genet, 55(5):876-882.
[16] Shammas MA (2011). Telomeres, lifestyle, cancer, and aging. Curr Opin Clin Nutr Metab Care, 14(1):28-34.
[17] Ehrlenbach S, Willeit P, Kiechl S, Willeit J, Reindl M, Schanda K,et al (2009). Influences on the reduction of relative telomere length over 10 years in the population-based Bruneck Study: introduction of a well-controlled high-throughput assay. Int J Epidemiol, 38(6):1725-1734.
[18] Morla M, Busquets X, Pons J, Sauleda J, MacNee W, Agust¡ AGN (2006). Telomere shortening in smokers with and without COPD. Eur Respir J, 27(3):525-528.
[19] Pavanello S, Hoxha M, Dioni L, Bertazzi PA, Snenghi R, Nalesso A,et al (2011). Shortened telomeres in individuals with abuse in alcohol consumption. Int J Cancer, 129(4):983-992.
[20] Lee JY, Jun NR, Yoon D, Shin C, Baik I (2015). Association between dietary patterns in the remote past and telomere length. Eur J Clin Nutr, 69(9):1048-1052.
[21] Cassidy A, De Vivo I, Liu Y, Han J, Prescott J, Hunter DJ,et al (2010). Associations between diet, lifestyle factors, and telomere length in women. Am J Clin Nutr, 91(5):1273-1280.
[22] Valdes AM, Andrew T, Gardner JP, Kimura M, Oelsner E, Cherkas LF,et al (2005). Obesity, cigarette smoking, and telomere length in women. Lancet, 366(9486):662-664.
[23] Epel ES, Blackburn EH, Lin J, Dhabhar FS, Adler NE, Morrow JD,et al (2004). Accelerated telomere shortening in response to life stress. Proc Natl Acad Sci U S A, 101(49):17312-17315.
[24] Mallat SG, Samra SA, Younes F, Sawaya MT (2014). Identifying predictors of blood pressure control in the Lebanese population - a national, multicentric survey - I-PREDICT. BMC Public Health, 14:1142.
[25] Costanian C, Bennett K, Hwalla N, Assaad S, Sibai AM (2014). Prevalence, correlates and management of type 2 diabetes mellitus in Lebanon: findings from a national population-based study. Diabetes Res Clin Pract, 105(3):408-415.
[26] Sibai AM, Costanian C, Tohme R, Assaad S, Hwalla N (2013). Physical activity in adults with and without diabetes: from the ’high-risk’ approach to the ’population-based’ approach of prevention. BMC Public Health, 13:1002.
[27] Nasreddine L, Naja F, Chamieh MC, Adra N, Sibai AM, Hwalla N (2012). Trends in overweight and obesity in Lebanon: evidence from two national cross-sectional surveys (1997 and 2009). BMC, 12:798.
[28] Naja F, Nasreddine L, Itani L, Chamieh MC, Adra N, Sibai AM,et al (2011). Dietary patterns and their association with obesity and sociodemographic factors in a national sample of Lebanese adults. Public Health Nutr, 14(9):1570-1578.
[29] Chamieh MC, Moore HJ, Summerbell C, Tamim H, Sibai AM, Hwalla N (2015). Diet, physical activity and socio-economic disparities of obesity in Lebanese adults: findings from a national study. BMC Public Health, 15:279.
[30] Al-Attas OS, Al-Daghri NM, Alokail MS, Alkharfy KM, Alfadda AA, McTernan P,et al (2012). Circulating leukocyte telomere length is highly heritable among families of Arab descent. BMC Med Genet, 13:38.
[31] Al-Attas OS, Al-Daghri NM, Alokail MS, Alfadda A, Bamakhramah A, Sabico S,et al (2010). Adiposity and insulin resistance correlate with telomere length in middle-aged Arabs: the influence of circulating adiponectin. Eur J Endocrinol, 163(4):601-607.
[32] Al-Attas OS, Al-Daghri N, Bamakhramah A, Shaun SS, McTernan P, Huang TT (2010). Telomere length in relation to insulin resistance, inflammation and obesity among Arab youth. Acta Paediatr, 99(6):896-899.
[33] Kark JD, Nassar H, Shaham D, Sinnreich R, Goldberger N, Aboudi V,et al (2013). Leukocyte telomere length and coronary artery calcification in Palestinians. Atherosclerosis, 229(2):363-368.
[34] Houben JM, Moonen HJ, van Schooten FJ, Hageman GJ (2008). Telomere length assessment: biomarker of chronic oxidative stress? Free Radic Biol Med, 44(3):235-246.
[35] Lee PH, Macfarlane DJ, Lam TH, Stewart SM (2011). Validity of the International Physical Activity Questionnaire Short Form (IPAQ-SF): a systematic review. Int J Behav Nutr Phys Act, 8:115.
[36] Cawthon RM (2002). Telomere measurement by quantitative PCR. Nucleic Acids Res, 30(10):e47.
[37] Cawthon RM (2009). Telomere length measurement by a novel monochrome multiplex quantitative PCR method. Nucleic Acids Res, 37(3):e21.
[38] Pfaffl MW (2001). A new mathematical model for relative quantification in real-time RT-PCR. Nucleic Acids Res, 29(9):e45.
[39] Standards of medical care in diabetes-2015: summary of revisionsDiabetes Care 2015 Jan;38 Suppl:S4.
[40] Armstrong C (2014). JNC8 guidelines for the management of hypertension in adults. Am Fam Physician, 90(7):503-504.
[41] Stone NJ, Robinson JG, Lichtenstein AH, Bairey Merz CN, Blum CB, Eckel RH,et al (2014). 2013 ACC/AHA guideline on the treatment of blood cholesterol to reduce atherosclerotic cardiovascular risk in adults: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. J Am Coll Cardiol, 63(25 Pt B):2889-934.
[42] Alberti KG, Eckel RH, Grundy SM, Zimmet PZ, Cleeman JI, Donato KA,et al (2009). Harmonizing the metabolic syndrome: a joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity. Circulation, 120(16):1640-1645.
[43] Goff DCJr., Lloyd-Jones DM, Bennett G, Coady S, D’Agostino RB, Gibbons R,et al (2014). 2013 ACC/AHA guideline on the assessment of cardiovascular risk: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. Circulation, 129(25 Suppl 2):S49-S73.
[44] Diez Roux AV, Ranjit N, Jenny NS, Shea S, Cushman M, Fitzpatrick A,et al (2009). Race/ethnicity and telomere length in the Multi-Ethnic Study of Atherosclerosis. Aging Cell, 8(3):251-257.
[45] Zhu H, Wang X, Gutin B, Davis CL, Keeton D, Thomas J,et al (2011). Leukocyte telomere length in healthy Caucasian and African-American adolescents: relationships with race, sex, adiposity, adipokines, and physical activity. J Pediatr, 158(2):215-220.
[46] Zhang WG, Zhu SY, Bai XJ, Zhao DL, Jiang SM, Li J,et al (2014). Select aging biomarkers based on telomere length and chronological age to build a biological age equation. Age (Dordr), 36(3):9639.
[47] Benetos A, Okuda K, Lajemi M, Kimura M, Thomas F, Skurnick J,et al (2001). Telomere Length as an Indicator of Biological Aging: The Gender Effect and Relation With Pulse Pressure and Pulse Wave Velocity. Hypertension, 37(2):381-385.
[48] Gardner M, Bann D, Wiley L, Cooper R, Hardy R, Nitsch D,et al (2014). Gender and telomere length: Systematic review and meta-analysis. Exp Gerontol, 51:15-27.
[49] Moller P, Mayer S, Mattfeldt T, Muller K, Wiegand P, Bruderlein S (2009). Sex-related differences in length and erosion dynamics of human telomeres favor females. Aging (Albany NY), 1(8):733-739.
[50] Lee M, Martin H, Firpo MA, Demerath EW (2011). Inverse Association Between Adiposity and Telomere Length: The Fels Longitudinal Study. Am J Hum Biol, 23(1):100-106.
[51] Kim S, Parks CG, DeRoo LA, Chen H, Taylor JA, Cawthon RM,et al (2009). Obesity and Weight Gain in Adulthood and Telomere Length. Cancer Epidemiol Biomarkers Prev, 18(3):816.
[52] Fitzpatrick AL, Kronmal RA, Gardner JP, Psaty BM, Jenny NS, Tracy RP,et al (2007). Leukocyte Telomere Length and Cardiovascular Disease in the Cardiovascular Health Study. Am J Epidemiol, 165(1):14-21.
[53] Brenner DR, Tepylo K, Eny KM, Cahill LE, El-Sohemy A (2010). Comparison of body mass index and waist circumference as predictors of cardiometabolic health in a population of young Canadian adults. Diabetol Metab Syndr, 2(1):1-8.
[54] Ravensbergen HRJC, Lear SA, Claydon VE (2014). Waist Circumference Is the Best Index for Obesity-Related Cardiovascular Disease Risk in Individuals with Spinal Cord Injury. J Neurotrauma, 31(3):292-300.
[55] Janssen I, Katzmarzyk PT, Ross R (2004). Waist circumference and not body mass index explains obesity-related health risk. Am J Clin Nutr, 79(3):379-84.
[56] Shen W, Punyanitya M, Chen J, Gallagher D, Albu J, Pi-Sunyer X,et al (2006). Waist circumference correlates with metabolic syndrome indicators better than percentage fat. Obesity (Silver Spring), 14.
[57] Dagan SS, Segev S, Novikov I, Dankner R (2013). Waist circumference vs body mass index in association with cardiorespiratory fitness in healthy men and women: a cross sectional analysis of 403 subjects. Nutr J, 12:12.
[58] Ross R, Berentzen T, Bradshaw AJ, Janssen I, Kahn HS, Katzmarzyk PT,et al (2008). Does the relationship between waist circumference, morbidity and mortality depend on measurement protocol for waist circumference?. Obes. Rev, 9(4):312-25.
[59] Mason C, Katzmarzyk PT (2009). Effect of the Site of Measurement of Waist Circumference on the Prevalence of the Metabolic Syndrome. Am J Cardiol, 103(12):1716-1720.
[60] Prather AA, Puterman E, Lin J, O’Donovan A, Krauss J, Tomiyama AJ,et al (2011). Shorter Leukocyte Telomere Length in Midlife Women with Poor Sleep Quality. J Aging Res, 721390.
[61] Liang G, Schernhammer E, Qi L, Gao X, De Vivo I, Han J (2011). Associations between Rotating Night Shifts, Sleep Duration, and Telomere Length in Women. PLoS One, 6(8):e23462.
[62] Jackowska M, Hamer M, Carvalho LA, Erusalimsky JD, Butcher L, Steptoe A (2012). Short Sleep Duration Is Associated with Shorter Telomere Length in Healthy Men: Findings from the Whitehall II Cohort Study. PLoS One, 7(10):e47292.
[63] Adler N, Pantell M, O?ÇÖDonovan A, Blackburn E, Cawthon R, Koster A,et al (2013). Educational Attainment and Late Life Telomere Length in the Health, Aging and Body Composition Study. Brain Behav Immun, 27(1):15-21.
[64] Babizhayev MA, Savel’yeva EL, Moskvina SN, Yegorov YE (2011). Telomere Length is a Biomarker of Cumulative Oxidative Stress, Biologic Age, and an Independent Predictor of Survival and Therapeutic Treatment Requirement Associated With Smoking Behavior. Am J Ther, 18(6).
[65] Furukawa S, Fujita T, Shimabukuro M, Iwaki M, Yamada Y, Nakajima Y,et al (2004). Increased oxidative stress in obesity and its impact on metabolic syndrome. J Clin Invest, 114(12):1752-1761.
[66] Giordano FJ (2005). Oxygen, oxidative stress, hypoxia, and heart failure. J Clin Invest, 115(3):500-508.
[67] Anand IS, Latini R, Florea VG, Kuskowski MA, Rector T, Masson S,et al (2005). C-reactive protein in heart failure: prognostic value and the effect of valsartan. Circulation, 112(10):1428-1434.
[68] Harrison D, Griendling KK, Landmesser U, Hornig B, Drexler H (2003). Role of oxidative stress in atherosclerosis. Am J Cardiol, 91(3A):7A-11A.
[69] Sampson MJ, Winterbone MS, Hughes JC, Dozio N, Hughes DA (2006). Monocyte telomere shortening and oxidative DNA damage in type 2 diabetes. Diabetes Care, 29(2):283-289.
[70] Weinbrenner T, Schroder H, Escurriol V, Fito M, Elosua R, Vila J,et al (2006). Circulating oxidized LDL is associated with increased waist circumference independent of body mass index in men and women. Am J Clin Nutr, 83(1):30-35.
[71] Von ZT (2002). Oxidative stress shortens telomeres. Trends Biochem Sci, 27(7):339-344.
[72] Werner C, Furster T, Widmann T, Poss J, Roggia C, Hanhoun M,et al (2009). Physical Exercise Prevents Cellular Senescence in Circulating Leukocytes and in the Vessel Wall. Circulation, 120(24):2438-2447.
[73] Chang YS, Chou YT, Lee JH, Lee PL, Dai YS, Sun C,et al (2014). Atopic Dermatitis, Melatonin, and Sleep Disturbance. Pediatrics, 134(2):e397-e405.
[74] Radogna F, Diederich M, Ghibelli L (2010). Melatonin: A pleiotropic molecule regulating inflammation. Biochem Pharmacol, 80(12):1844-1852.
[75] Prather AA, Marsland AL, Hall M, Neumann SA, Muldoon MF, Manuck SB (2009). Normative Variation in Self-reported Sleep Quality and Sleep Debt is Associated with Stimulated Pro-inflammatory Cytokine Production. Biol Psychol, 82(1):12-17.
[76] Lasikiewicz N, Hendrickx H, Talbot D, Dye L (2008). Exploration of basal diurnal salivary cortisol profiles in middle-aged adults: Associations with sleep quality and metabolic parameters. Psychoneuroendocrinology, 33(2):143-151.
[77] Blake GJ, Rifai N, Buring JE, Ridker PM (2003). Blood Pressure, C-Reactive Protein, and Risk of Future Cardiovascular Events. Circulation, 108(24):2993-2999.
[78] Bautista LE, Atwood JE, O’Malley PG, Taylor AJ (2004). Association between C-reactive protein and hypertension in healthy middle-aged men and women. Coron Artery Dis, 15(6).
[79] Kim KS, Kwak JW, Lim SJ, Park YK, Yang HS, Kim HJ (2016). Oxidative Stress-induced Telomere Length Shortening of Circulating Leukocyte in Patients with Obstructive Sleep Apnea. Aging Dis, 7(5):604-613
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