Inverse relationship between leukocyte telomere length attrition and blood mitochondrial DNA content loss over time

Results

Study sample

Characteristics of the study participants are shown in Table 1. Cross-sectional analyses included 312 women living with HIV (WLWH) and 300 HIV-negative participants. WLWH were slightly younger, with a median (range) age of 41 (14-69) vs. 44 (15-78) years (P=0.042). WLWH were also more likely to be African/Caribbean/Black (ACB), current tobacco smokers, current prescribed opioid users, have past or present HBV or HCV infections, and less likely to be White, Asian, current drinkers, have a household income >15000 CAD/year, and have any college education compared to HIV-negative controls. Body mass index and cannabis use did not differ between groups. Among WLWH, 70% of participants had an undetectable HIV plasma viral load (pVL <50 HIV RNA copies/ml) and 82% were on cART at visit. Among all participants, the median[interquartile range] LTL and WB mtDNA content measurements were 7.2[6.5-7.9] and 105[81-139], respectively.

Table 1. Study sample characteristics.

	Cross-Sectional Sample (N=612)			Longitudinal Sample (N=296)
	HIV+ (N=312)	HIV- (N=300)	P-value	HIV+ (N=228)	HIV- (N=68)	P-value
Age, years	41 [31,50](14,69)	44 [31,55](15,78)	0.042	38 [29,46](12,67)	36 [27,48](12,73)	0.859
Time between visits, years				4 [2,6] (1,8)	3 [2,5] (1,7)	< 0.001
BMI, kg/m2 (N=603)	24.9 [21.4,30.0] (15.0,48.6)	24.6 [21.4,29.7] (14.0,52.9)	0.608	24.0 [21.3,28.8] (16.0,46.6)	23.5 [20.5,28.7] (15.3,42.3)	0.526
Ethnicity			< 0.001
White	131 (42)	153 (51)		94 (41)	37 (54)	0.003
African/Black/Caribbean	72 (23)	19 (6)		51 (22)	3 (4)
Indigenous	78 (25)	79 (26)		58 (25)	16 (24)
Asian	8 (3)	28 (9)		8 (4)	7 (10)
Othera	23 (7)	21 (7)		17 (7)	5 (7)
Household income >$15000 CAD/year (N=553)	127 (48)	170 (59)	0.009	95 (48)	40 (67)	0.011
Highest level of education (N=552) Any College	114 (43)	200 (69)	< 0.001	93 (47)	44 (73)	0.005
High School Graduate	54 (21)	30 (10)		38 (19)	6 (10)
Some High School	80 (30)	53 (18)		56 (28)	9 (15)
Grade School	15 (6)	6 (2)		11 (6)	1 (2)
Cigarette smokingb
Current	122 (39)	85 (28)	0.010	85 (37)	13 (19)	0.002
Past	69 (22)	67 (22)		54 (24)	12 (18)
Never	121 (39)	148 (49)		89 (39)	43 (63)
Cannabisb
Current	77 (25)	72 (24)	0.853	59 (26)	15 (22)	0.664
Past	67 (21)	60 (20)		49 (21)	13 (19)
Never	168 (54)	168 (56)		120 (53)	40 (59)
Alcoholb
Current	150 (48)	199 (66)	< 0.001	113 (50)	44 (65)	0.039
Past	87 (28)	61 (20)		63 (28)	17 (25)
Never	75 (24)	40 (13)		52 (23)	7 (10)
Current any opioid useb	69 (22) 62 (20)	32 (11) 25 (8)	< 0.001	51 (22) 47 (21)	3 (4) 1 (1)	< 0.001
Prescribed opioid use			< 0.001			< 0.001
Heroin use	17 (5)	13 (4)	0.523	15 (7)	0 (0)	0.030
HIV Detectable pVL at visit, >50 copies/mlb,c	95 (30)			63 (28)
HIV Peak pVL >100000 copies/ml (N=248)	111 (45)			87 (48)
CD4 count at visit, cells/μLb	540 [348,733] (10,2380)			500 [348,683] (14,1370)
CD4 nadir, cells/μL (N=260)	221 [130,350] (1,1110)			216 [118,330] (1,900)
ART-naïve (N=278)	10 (4)			11 (5)
On cART at visitb	255 (82)			195 (86)
HBV ever infected (N=395)	29 (13)	2 (1)	< 0.001	9 (26)	0 (0)	0.011
HCV ever infected	109 (35)	45 (15)	< 0.001	79 (35)	9 (13)	< 0.001
Platelet Count, 109/L (N=429)	227 [178,274] (40,663)	235 [213,281] (82,464)	0.005	233 [184,274] (45,489)	272 [217,277] (172,316)	0.240
Relative LTL	7.1 [6.4,7.8] (4.8,11.5)	7.4 [6.7,8.1] (4.8,11.3)	0.004	7.0 [6.3,7.8] (4.7,10.5)	7.5 [6.9,8.4] (5.1,10.5)	< 0.001
ΔLTL/year			0.004	0.01 [-0.10,0.11] (-0.94,1.80)	-0.05 [-0.26,0.09] (-1.18,0.74)	0.020
WB mtDNA content	101 [78,131] (4,265)	112 [85,146] (4,379)		143 [109,171] (4,277)	129 [96,154] (4,231)	0.025
WB ΔmtDNA content/year				-10 [-15,-1] (-76,77)	-7 [-16,8] (-32,37)	0.013
Data are number (%) of individuals or median [interquartile range] (range). Number of participants with available data indicated beside variables with incomplete data. P-values indicate Mann-Whitney U or Chi-Squared Tests depending on type of variable.
Abbreviations: ART, antiretroviral therapy; BMI, body mass index; cART, combination antiretroviral therapy; CAD, Canadian dollars; HBV, hepatitis B virus; HCV, hepatitis C virus; LTL, leukocyte telomere length; mtDNA, mitochondrial DNA; pVL, plasma viral load; WB, whole blood.
a a combination of South Asian, Hispanic, or other
b at last visit for longitudinal sample
c 22 lost HIV viral control and 41 participants gained HIV viral control between longitudinal visits

Cross-sectional LTL and WB mtDNA content decline with age but are not inter-related

WLWH had shorter LTL (median 7.1 vs.7.4) and lower WB mtDNA content (median 101 vs. 112) than HIV-negative controls before (Table 1, P≤0.004) and after adjusting for age (P≤0.002). Among all participants, relative LTL declined with age (Figure 1A), with an average loss of approximately 33 bp of telomeric DNA per year (R²=0.17, P<0.0001). LTL was modulated by HIV status, whereby the decline in LTL with age was faster among WLWH with detectable pVL compared to HIV-negative controls. WB mtDNA also declined with age (Figure 1B), although with a weaker relationship (R²=0.03, P<0.001) that was not modulated by HIV. Despite the fact that both markers declined with age, they were not associated with one another (Figure 1C) (R²=0.004, P=0.14).

Figure 1. Cross-sectional univariate associations between leukocyte telomere length (LTL), whole blood mitochondrial DNA (WB mtDNA) content, and age. Among 612 participants, (A) LTL declines with age (R²=0.17, Slope=-0.03, Pearson’s P<0.0001); as does (B) WB mtDNA content (R²=0.03, Slope=-0.57, P<0.0001). Both measures also decline within each HIV subgroup (all p<0.01). Linear regressions of HIV-, HIV+ undetectable plasma viral load (pVL), and HIV+ with detectable pVL participants are shown in blue, orange, and magenta, respectively. Differences between slopes were tested and showed that (A) LTL declines faster among HIV+ detectable pVL participants than in HIV- controls. (C) No detectable relationship exists between LTL and WB mtDNA content (P=0.138). The shaded area indicates the 95% prediction interval. Coefficients of determination (R²) are shown.

Cross-sectional predictors of LTL

Among all participants, the final multivariable model for LTL (N=612, R²=0.28) indicated that HIV infection (either with detectable or undetectable pVL), current tobacco smoking, and older age were independently associated with shorter LTL. In contrast, alcohol use and ACB or Asian ethnicities were associated with longer LTL (Figure 2A). Cannabis use showed no effect on LTL. In a subgroup of WLWH (Figure 2B), detectable HIV pVL was also independently associated with shorter LTL compared to undetectable HIV pVL (N=312, R²=0.31), in addition to the above factors. Among HIV-negative participants, current smoking remained associated (N=300, R²=0.28) with shorter LTL, but current alcohol did not (Figure 2C). Models showing non-standardized effect sizes are shown in supplement (Supplementary Figure 2). Although substance use was considered when building our model, to ensure the validity of our findings in the general population, we performed subgroup analyses among participants who never used tobacco, cannabis, or opioids (Supplementary Figure 3A–3C). Similarly, informed by past studies that showed effects on LTL by chronic co-infections [12], we analyzed participants with no history of either HCV or HBV infection (Supplementary Figure 3D). All models showed essentially the same effects as the main model, and in the smaller never smoker and never HBV/HCV-coinfected groups, the effect size of undetectable HIV infection was similar, but the confidence interval became broader and significance was lost. The effect of detectable HIV infection remained in all subgroup models. Taken together, these secondary models confirmed the robustness of the effects observed in our main model.

Figure 2. Multivariable modelling of cross-sectional leukocyte telomere length (LTL) and whole blood mitochondrial DNA (WB mtDNA) content. Final selected multivariable linear regression models of cross-sectional LTL (variance inflation factor (VIF) ≤2.1) in (A) all, (B) HIV+, and (C) HIV- participants, and WB mtDNA content (VIF≤1.3) in (D) all, (E) HIV+, and (F) HIV- participants. Final models among all participants were selected automatically by minimizing Akaike’s Information Criterion (AIC). Statistical significance depicted by red confidence intervals; negative standardized β values indicate associations with either shorter LTL or lower WB mtDNA content and vice versa. Coefficients of determination (R²) are shown for each model. The models show that after adjusting for age, ethnicity and substance use, HIV infection is independently associated with shorter LTL and lower WB mtDNA content. Detectable HIV viremia was associated with shorter LTL but not WB mtDNA content.

Cross-sectional predictors of WB mtDNA content

The final multivariable model for WB mtDNA content (N=612, R²=0.10) indicated that HIV infection and older age were independently associated with decreased WB mtDNA content, while ACB and Indigenous ethnicities, as well as current alcohol use, showed an association with increased WB mtDNA content (Figure 2D). Contrary to the LTL models, no difference was detected between participants with detectable or undetectable HIV pVL (Figure 2E). Once again, we carried out subgroup analyses as above and found that older age remained in all models while the effect of HIV was detected sporadically, likely due to lower power and wider confidence intervals (Supplementary Figure 4).

Sensitivity models were constructed including platelet count data that were available for a subset of study participants. Here, higher platelet count showed an association with higher WB mtDNA content (Supplementary Figure 5A–5C) and the relationship between older age and decreased WB mtDNA content remained, but the effect of HIV was lost. However, in these models, the sample size of the HIV-negative group was considerably reduced (N=121 vs. N=300).

Longitudinal changes in LTL and mtDNA are inversely related

Having determined that both age and HIV affect our two markers cross-sectionally, we then examined the dynamics of the two markers over time, as well as their longitudinal relationship. Of the 612 participants studied cross-sectionally, 228 WLWH and 68 HIV-negative participants had at least one visit ≥1 year earlier and were included in the longitudinal analyses. The median time between longitudinal visits was 4 and 3 years for WLWH and HIV-negative participants, respectively. Characteristics of the longitudinal subset were similar to whole group (Table 1) with the exception that age was similar between groups, and baseline mtDNA content was higher in WLWH than HIV-negative controls. Medians [interquartile range] of 4 [2, 6], and 3 [2, 5] years elapsed between longitudinal visits in WLWH and HIV-negative controls, respectively.

A significant negative relationship was observed between ΔLTL/year and WB ΔmtDNA content/year (R²=0.13, P<0.001) (Figure 3A). Overall, participants who lost LTL the fastest were also most likely to have gained mtDNA over the same period (Figure 3B) and vice versa whereby those who lost mtDNA the fastest were more likely to have gained LTL (Figure 3C).

Figure 3. Univariate associations between longitudinal rates of change in leukocyte telomere length (LTL) and whole blood mitochondrial DNA (WB mtDNA) content. (A) Data show an inverse relationship between ΔLTL/year and WB ΔmtDNA content/year. Shaded area represents the 95% prediction interval. Coefficient of determination (R²) and Pearson's P-value shown. (B) Participants were categorized based on a small (<0.1) or large (>0.1) loss or gain of ΔLTL/year and further stratified according to a small (<10) or large (>10) loss or gain of WB ΔmtDNA content/year. These data show that participants who lost LTL fastest were more likely to preserve or gain WB mtDNA content. (C) Similarly, participants were categorized based on WB ΔmtDNA content/year and stratified according to ΔLTL/year. The data show that participants who lost WB mtDNA fastest were more likely to preserve LTL and vice versa.

Predictors of longitudinal change in LTL

In the univariate analysis, overall relative LTL shortened by 0.036 units/year among all participants. This represents a loss of approximately 36 bp/year, in close agreement with the population attrition rate estimate (33 bp/year) seen in the cross-sectional analysis (Figure 1A). These univariate investigations were validated by multivariable modelling (N=296, R²=0.33), in which we observed that a slower decrease in WB mtDNA content/year was a strong predictor of faster LTL loss (Figure 4A), as was having longer LTL at baseline. Older age at baseline was also associated with faster LTL loss while past alcohol use, current opioid use, as well as ACB and Asian ethnicities were associated with slower loss of LTL (Figure 4A), reminiscent of the cross-sectional analysis. In contrast to the cross-sectional finding, tobacco smoking and undetectable HIV did not show an effect on LTL attrition rate. However, among WLWH, LTL was lost faster in those with a detectable HIV pVL at last visit (Figure 4B). Among HIV-negative participants (Figure 4C), only age, baseline LTL and WB ΔmtDNA content were included due to the reduced sample, and they all showed similar effects as those seen in the all-participants model. Models showing non-standardized effect sizes are shown in supplement (Supplementary Figure 6). In all subgroup analyses that excluded substance users or co-infections (Supplementary Figure 7), slower loss of WB mtDNA content/year and longer baseline LTL remained independently associated with faster LTL attrition. Similar to the cross-sectional subgroup models, the effect of HIV lost significance in some models, likely due to reduced power.

Figure 4. Multivariable modelling of longitudinal rates of change in leukocyte telomere length (LTL) and whole blood mitochondrial DNA (WB mtDNA) content. Final selected multivariable linear regression models of longitudinal ΔLTL/year (variance inflation factor (VIF) ≤1.5) in (A) all, (B) HIV+, and (C) HIV- participants, and WB ΔmtDNA content/year (VIF≤1.2) in (D) all, (E) HIV+, and (F) HIV- participants. Final models among all participants were selected automatically by minimizing Akaike’s Information Criterion (AIC). Statistical significance depicted by red confidence intervals; negative standardized β values indicate associations with either faster LTL loss or faster WB mtDNA content loss and vice versa. Coefficients of determination (R²) are shown for each model. The ΔLTL/year models show that after adjusting for age and LTL at baseline, ethnicity, and substance use, a slower loss of WB mtDNA/year is significantly independently associated with faster LTL attrition. Similarly, the models for WB ΔmtDNA content/year show that after adjusting for LTL and WB mtDNA at baseline, a slower rate of LTL attrition is independently associated with faster loss of WB mtDNA content. Detectable HIV viremia was associated with faster decline in both markers.

Taken together, our data show that the main predictors of faster LTL attrition were having longer LTL at baseline and a slower WB mtDNA content decline over time. Our data also point toward faster LTL attrition among those who have a detectable HIV pVL while those who maintain viral control showed rates of LTL loss essentially identical to HIV-negative individuals.

Predictors of longitudinal change in WB mtDNA content

In WLWH and controls, WB mtDNA content decreased by medians of 10 and 7 copies of mtDNA per copy of nDNA/year, respectively (Table 1). Similar to what was observed for ΔLTL, in the final model of WB ΔmtDNA content (N=296, R²=0.39), detectable HIV infection, higher WB mtDNA content at baseline, and slower LTL attrition rate were each independently associated with a faster decline in WB mtDNA content (Figure 4D). Among WLWH (Figure 4E) and HIV-negative controls (Figure 4F), the same associations were seen. Furthermore, compared to WLWH with undetectable HIV pVL, having a detectable HIV pVL at last visit was associated with faster WB mtDNA content decline (Figure 4E). Subgroup analyses were highly similar to the all-participants model (Supplementary Figure 8) and the effect of viremia persisted in all models. Adding platelet counts to the WB ΔmtDNA content models had no effect, and platelet count itself showed no association with WB ΔmtDNA content (Supplementary Figure 9).

Overall, as seen for the change in LTL, the strongest predictors of faster decrease in WB mtDNA content were higher WB mtDNA content at baseline and slower LTL loss. In addition, having a detectable HIV pVL accelerated mtDNA loss.

Acknowledgments

The authors thank all study participants, collaborators and research staff at CARMA, as well as members of the Côté Lab for their guidance and support.

Conflicts of Interest

The authors have no conflicts of interest to disclose.

Funding

This work was supported by the Canadian Institutes of Health Research (CIHR) Team Grant on Cellular Aging and HIV Comorbidities in Women and Children 2013-2018 TCO-125269 (to HCFC). The authors acknowledge additional support from the University of British Columbia (UBC) Centre for Blood Research (CBR) Summer Studentship (to EK), and the CBR Graduate Student Award, UBC Four Year Doctoral Fellowship, and CIHR Doctoral Research Award (to AYYH).

References

• 1. Müezzinler A, Zaineddin AK, Brenner H. A systematic review of leukocyte telomere length and age in adults. Ageing Res Rev. 2013; 12:509–19. https://doi.org/10.1016/j.arr.2013.01.003 [PubMed]
• 2. Frenck RW Jr, Blackburn EH, Shannon KM. The rate of telomere sequence loss in human leukocytes varies with age. Proc Natl Acad Sci USA. 1998; 95:5607–10. https://doi.org/10.1073/pnas.95.10.5607 [PubMed]
• 3. Daniali L, Benetos A, Susser E, Kark JD, Labat C, Kimura M, Desai K, Granick M, Aviv A. Telomeres shorten at equivalent rates in somatic tissues of adults. Nat Commun. 2013; 4:1597. https://doi.org/10.1038/ncomms2602 [PubMed]
• 4. Hjelmborg JB, Dalgård C, Möller S, Steenstrup T, Kimura M, Christensen K, Kyvik KO, Aviv A. The heritability of leucocyte telomere length dynamics. J Med Genet. 2015; 52:297–302. https://doi.org/10.1136/jmedgenet-2014-102736 [PubMed]
• 5. Gardner M, Bann D, Wiley L, Cooper R, Hardy R, Nitsch D, Martin-Ruiz C, Shiels P, Sayer AA, Barbieri M, Bekaert S, Bischoff C, Brooks-Wilson A, et al, and Halcyon study team. Gender and telomere length: systematic review and meta-analysis. Exp Gerontol. 2014; 51:15–27. https://doi.org/10.1016/j.exger.2013.12.004 [PubMed]
• 6. Hunt SC, Chen W, Gardner JP, Kimura M, Srinivasan SR, Eckfeldt JH, Berenson GS, Aviv A. Leukocyte telomeres are longer in African Americans than in whites: the national heart, lung, and blood institute family heart study and the bogalusa heart study. Aging Cell. 2008; 7:451–58. https://doi.org/10.1111/j.1474-9726.2008.00397.x [PubMed]
• 7. Cassidy A, De Vivo I, Liu Y, Han J, Prescott J, Hunter DJ, Rimm EB. Associations between diet, lifestyle factors, and telomere length in women. Am J Clin Nutr. 2010; 91:1273–80. https://doi.org/10.3945/ajcn.2009.28947 [PubMed]
• 8. Ludlow AT, Zimmerman JB, Witkowski S, Hearn JW, Hatfield BD, Roth SM. Relationship between physical activity level, telomere length, and telomerase activity. Med Sci Sports Exerc. 2008; 40:1764–71. https://doi.org/10.1249/MSS.0b013e31817c92aa [PubMed]
• 9. Astuti Y, Wardhana A, Watkins J, Wulaningsih W, and PILAR Research Network. Cigarette smoking and telomere length: a systematic review of 84 studies and meta-analysis. Environ Res. 2017; 158:480–89. https://doi.org/10.1016/j.envres.2017.06.038 [PubMed]
• 10. Müezzinler A, Mons U, Dieffenbach AK, Butterbach K, Saum KU, Schick M, Stammer H, Boukamp P, Holleczek B, Stegmaier C, Brenner H. Smoking habits and leukocyte telomere length dynamics among older adults: results from the ESTHER cohort. Exp Gerontol. 2015; 70:18–25. https://doi.org/10.1016/j.exger.2015.07.002 [PubMed]
• 11. Yang Z, Ye J, Li C, Zhou D, Shen Q, Wu J, Cao L, Wang T, Cui D, He S, Qi G, He L, Liu Y. Drug addiction is associated with leukocyte telomere length. Sci Rep. 2013; 3:1542. https://doi.org/10.1038/srep01542 [PubMed]
• 12. Zanet DL, Thorne A, Singer J, Maan EJ, Sattha B, Le Campion A, Soudeyns H, Pick N, Murray M, Money DM, Côté HC, and CIHR Emerging Team Grant on HIV Therapy and Aging: CARMA. Association between short leukocyte telomere length and HIV infection in a cohort study: no evidence of a relationship with antiretroviral therapy. Clin Infect Dis. 2014; 58:1322–32. https://doi.org/10.1093/cid/ciu051 [PubMed]
• 13. Bestilny LJ, Gill MJ, Mody CH, Riabowol KT. Accelerated replicative senescence of the peripheral immune system induced by HIV infection. AIDS. 2000; 14:771–80. https://doi.org/10.1097/00002030-200005050-00002 [PubMed]
• 14. Gonzalez-Serna A, Ajaykumar A, Gadawski I, Muñoz-Fernández MA, Hayashi K, Harrigan PR, Côté HC. Rapid decrease in peripheral blood mononucleated cell telomere length after HIV seroconversion, but not HCV seroconversion. J Acquir Immune Defic Syndr. 2017; 76:e29–32. https://doi.org/10.1097/QAI.0000000000001446 [PubMed]
• 15. Leung JM, Fishbane N, Jones M, Morin A, Xu S, Liu JC, MacIsaac J, Milloy MJ, Hayashi K, Montaner J, Horvath S, Kobor M, Sin DD, et al. Longitudinal study of surrogate aging measures during human immunodeficiency virus seroconversion. Aging (Albany NY). 2017; 9:687–705. https://doi.org/10.18632/aging.101184 [PubMed]
• 16. Salpea KD, Talmud PJ, Cooper JA, Maubaret CG, Stephens JW, Abelak K, Humphries SE. Association of telomere length with type 2 diabetes, oxidative stress and UCP2 gene variation. Atherosclerosis. 2010; 209:42–50. https://doi.org/10.1016/j.atherosclerosis.2009.09.070 [PubMed]
• 17. Forero DA, González-Giraldo Y, López-Quintero C, Castro-Vega LJ, Barreto GE, Perry G. Meta-analysis of telomere length in Alzheimer’s disease. J Gerontol A Biol Sci Med Sci. 2016; 71:1069–73. https://doi.org/10.1093/gerona/glw053 [PubMed]
• 18. Willeit P, Willeit J, Mayr A, Weger S, Oberhollenzer F, Brandstätter A, Kronenberg F, Kiechl S. Telomere length and risk of incident cancer and cancer mortality. JAMA. 2010; 304:69–75. https://doi.org/10.1001/jama.2010.897 [PubMed]
• 19. Ma H, Zhou Z, Wei S, Liu Z, Pooley KA, Dunning AM, Svenson U, Roos G, Hosgood HD 3rd, Shen M, Wei Q. Shortened telomere length is associated with increased risk of cancer: a meta-analysis. PLoS One. 2011; 6:e20466. https://doi.org/10.1371/journal.pone.0020466 [PubMed]
• 20. Fitzpatrick AL, Kronmal RA, Gardner JP, Psaty BM, Jenny NS, Tracy RP, Walston J, Kimura M, Aviv A. Leukocyte telomere length and cardiovascular disease in the cardiovascular health study. Am J Epidemiol. 2007; 165:14–21. https://doi.org/10.1093/aje/kwj346 [PubMed]
• 21. Bekaert S, De Meyer T, Rietzschel ER, De Buyzere ML, De Bacquer D, Langlois M, Segers P, Cooman L, Van Damme P, Cassiman P, Van Criekinge W, Verdonck P, De Backer GG, et al, and Asklepios investigators. Telomere length and cardiovascular risk factors in a middle-aged population free of overt cardiovascular disease. Aging Cell. 2007; 6:639–47. https://doi.org/10.1111/j.1474-9726.2007.00321.x [PubMed]
• 22. van der Harst P, van der Steege G, de Boer RA, Voors AA, Hall AS, Mulder MJ, van Gilst WH, van Veldhuisen DJ, and MERIT-HF Study Group. Telomere length of circulating leukocytes is decreased in patients with chronic heart failure. J Am Coll Cardiol. 2007; 49:1459–64. https://doi.org/10.1016/j.jacc.2007.01.027 [PubMed]
• 23. Cawthon RM, Smith KR, O’Brien E, Sivatchenko A, Kerber RA. Association between telomere length in blood and mortality in people aged 60 years or older. Lancet. 2003; 361:393–95. https://doi.org/10.1016/S0140-6736(03)12384-7 [PubMed]
• 24. Mengel-From J, Thinggaard M, Dalgård C, Kyvik KO, Christensen K, Christiansen L. Mitochondrial DNA copy number in peripheral blood cells declines with age and is associated with general health among elderly. Hum Genet. 2014; 133:1149–59. https://doi.org/10.1007/s00439-014-1458-9 [PubMed]
• 25. Miró O, López S, Martínez E, Pedrol E, Milinkovic A, Deig E, Garrabou G, Casademont J, Gatell JM, Cardellach F. Mitochondrial effects of HIV infection on the peripheral blood mononuclear cells of HIV-infected patients who were never treated with antiretrovirals. Clin Infect Dis. 2004; 39:710–16. https://doi.org/10.1086/423176 [PubMed]
• 26. Côté HC, Brumme ZL, Craib KJ, Alexander CS, Wynhoven B, Ting L, Wong H, Harris M, Harrigan PR, O’Shaughnessy MV, Montaner JS. Changes in mitochondrial DNA as a marker of nucleoside toxicity in HIV-infected patients. N Engl J Med. 2002; 346:811–20. https://doi.org/10.1056/NEJMoa012035 [PubMed]
• 27. Sun J, Longchamps RJ, Piggott DA, Castellani CA, Sumpter JA, Brown TT, Mehta SH, Arking DE, Kirk GD. Association between HIV infection and mitochondrial DNA copy number in peripheral blood: a population-based, prospective cohort study. J Infect Dis. 2019; 219:1285–93. https://doi.org/10.1093/infdis/jiy658 [PubMed]
• 28. Lee HK, Song JH, Shin CS, Park DJ, Park KS, Lee KU, Koh CS. Decreased mitochondrial DNA content in peripheral blood precedes the development of non-insulin-dependent diabetes mellitus. Diabetes Res Clin Pract. 1998; 42:161–67. https://doi.org/10.1016/s0168-8227(98)00110-7 [PubMed]
• 29. Delbarba A, Abate G, Prandelli C, Marziano M, Buizza L, Arce Varas N, Novelli A, Cuetos F, Martinez C, Lanni C, Memo M, Uberti D. Mitochondrial alterations in peripheral mononuclear blood cells from Alzheimer’s disease and mild cognitive impairment patients. Oxid Med Cell Longev. 2016; 2016:5923938. https://doi.org/10.1155/2016/5923938 [PubMed]
• 30. Wang Y, Liu VW, Xue WC, Cheung AN, Ngan HY. Association of decreased mitochondrial DNA content with ovarian cancer progression. Br J Cancer. 2006; 95:1087–91. https://doi.org/10.1038/sj.bjc.6603377 [PubMed]
• 31. Meierhofer D, Mayr JA, Foetschl U, Berger A, Fink K, Schmeller N, Hacker GW, Hauser-Kronberger C, Kofler B, Sperl W. Decrease of mitochondrial DNA content and energy metabolism in renal cell carcinoma. Carcinogenesis. 2004; 25:1005–10. https://doi.org/10.1093/carcin/bgh104 [PubMed]
• 32. Chen S, Xie X, Wang Y, Gao Y, Xie X, Yang J, Ye J. Association between leukocyte mitochondrial DNA content and risk of coronary heart disease: a case-control study. Atherosclerosis. 2014; 237:220–26. https://doi.org/10.1016/j.atherosclerosis.2014.08.051 [PubMed]
• 33. Lee HC, Lu CY, Fahn HJ, Wei YH. Aging- and smoking-associated alteration in the relative content of mitochondrial DNA in human lung. FEBS Lett. 1998; 441:292–96. https://doi.org/10.1016/s0014-5793(98)01564-6 [PubMed]
• 34. Masayesva BG, Mambo E, Taylor RJ, Goloubeva OG, Zhou S, Cohen Y, Minhas K, Koch W, Sciubba J, Alberg AJ, Sidransky D, Califano J. Mitochondrial DNA content increase in response to cigarette smoking. Cancer Epidemiol Biomarkers Prev. 2006; 15:19–24. https://doi.org/10.1158/1055-9965.EPI-05-0210 [PubMed]
• 35. Hosgood HD 3rd, Liu CS, Rothman N, Weinstein SJ, Bonner MR, Shen M, Lim U, Virtamo J, Cheng WL, Albanes D, Lan Q. Mitochondrial DNA copy number and lung cancer risk in a prospective cohort study. Carcinogenesis. 2010; 31:847–49. https://doi.org/10.1093/carcin/bgq045 [PubMed]
• 36. Campa D, Barrdahl M, Santoro A, Severi G, Baglietto L, Omichessan H, Tumino R, Bueno-de-Mesquita HB, Peeters PH, Weiderpass E, Chirlaque MD, Rodríguez-Barranco M, Agudo A, et al. Mitochondrial DNA copy number variation, leukocyte telomere length, and breast cancer risk in the European Prospective Investigation into Cancer and Nutrition (EPIC) study. Breast Cancer Res. 2018; 20:29. https://doi.org/10.1186/s13058-018-0955-5 [PubMed]
• 37. Knez J, Winckelmans E, Plusquin M, Thijs L, Cauwenberghs N, Gu Y, Staessen JA, Nawrot TS, Kuznetsova T. Correlates of peripheral blood mitochondrial DNA content in a general population. Am J Epidemiol. 2016; 183:138–46. https://doi.org/10.1093/aje/kwv175 [PubMed]
• 38. Liu CS, Tsai CS, Kuo CL, Chen HW, Lii CK, Ma YS, Wei YH. Oxidative stress-related alteration of the copy number of mitochondrial DNA in human leukocytes. Free Radic Res. 2003; 37:1307–17. https://doi.org/10.1080/10715760310001621342 [PubMed]
• 39. Xing J, Chen M, Wood CG, Lin J, Spitz MR, Ma J, Amos CI, Shields PG, Benowitz NL, Gu J, de Andrade M, Swan GE, Wu X. Mitochondrial DNA content: its genetic heritability and association with renal cell carcinoma. J Natl Cancer Inst. 2008; 100:1104–12. https://doi.org/10.1093/jnci/djn213 [PubMed]
• 40. Sahin E, Colla S, Liesa M, Moslehi J, Müller FL, Guo M, Cooper M, Kotton D, Fabian AJ, Walkey C, Maser RS, Tonon G, Foerster F, et al. Telomere dysfunction induces metabolic and mitochondrial compromise. Nature. 2011; 470:359–65. https://doi.org/10.1038/nature09787 [PubMed]
• 41. Ahmed S, Passos JF, Birket MJ, Beckmann T, Brings S, Peters H, Birch-Machin MA, von Zglinicki T, Saretzki G. Telomerase does not counteract telomere shortening but protects mitochondrial function under oxidative stress. J Cell Sci. 2008; 121:1046–53. https://doi.org/10.1242/jcs.019372 [PubMed]
• 42. Haendeler J, Dröse S, Büchner N, Jakob S, Altschmied J, Goy C, Spyridopoulos I, Zeiher AM, Brandt U, Dimmeler S. Mitochondrial telomerase reverse transcriptase binds to and protects mitochondrial DNA and function from damage. Arterioscler Thromb Vasc Biol. 2009; 29:929–35. https://doi.org/10.1161/ATVBAHA.109.185546 [PubMed]
• 43. Kim JH, Kim HK, Ko JH, Bang H, Lee DC. The relationship between leukocyte mitochondrial DNA copy number and telomere length in community-dwelling elderly women. PLoS One. 2013; 8:e67227. https://doi.org/10.1371/journal.pone.0067227 [PubMed]
• 44. Tyrka AR, Carpenter LL, Kao HT, Porton B, Philip NS, Ridout SJ, Ridout KK, Price LH. Association of telomere length and mitochondrial DNA copy number in a community sample of healthy adults. Exp Gerontol. 2015; 66:17–20. https://doi.org/10.1016/j.exger.2015.04.002 [PubMed]
• 45. Zole E, Ranka R. Mitochondrial DNA copy number and telomere length in peripheral blood mononuclear cells in comparison with whole blood in three different age groups. Arch Gerontol Geriatr. 2019; 83:131–37. https://doi.org/10.1016/j.archger.2019.04.007 [PubMed]
• 46. Alegría-Torres JA, Velázquez-Villafaña M, López-Gutiérrez JM, Chagoyán-Martínez MM, Rocha-Amador DO, Costilla-Salazar R, García-Torres L. Association of leukocyte telomere length and mitochondrial DNA copy number in children from salamanca, Mexico. Genet Test Mol Biomarkers. 2016; 20:654–59. https://doi.org/10.1089/gtmb.2016.0176 [PubMed]
• 47. Deeks SG. HIV infection, inflammation, immunosenescence, and aging. Annu Rev Med. 2011; 62:141–55. https://doi.org/10.1146/annurev-med-042909-093756 [PubMed]
• 48. Eitan E, Braverman C, Tichon A, Gitler D, Hutchison ER, Mattson MP, Priel E. Excitotoxic and radiation stress increase TERT levels in the mitochondria and cytosol of cerebellar purkinje neurons. Cerebellum. 2016; 15:509–17. https://doi.org/10.1007/s12311-015-0720-6 [PubMed]
• 49. Spilsbury A, Miwa S, Attems J, Saretzki G. The role of telomerase protein TERT in Alzheimer’s disease and in tau-related pathology in vitro. J Neurosci. 2015; 35:1659–74. https://doi.org/10.1523/JNEUROSCI.2925-14.2015 [PubMed]
• 50. Davies RW, Morris B. Molecular Biology of the Neuron. Molecular and Cellular Neurobiology Series. Oxford: Oxford University Press. 2004. https://doi.org/10.1093/acprof:oso/9780198509981.001.0001
• 51. Pathai S, Lawn SD, Gilbert CE, McGuinness D, McGlynn L, Weiss HA, Port J, Christ T, Barclay K, Wood R, Bekker LG, Shiels PG. Accelerated biological ageing in HIV-infected individuals in South Africa: a case-control study. AIDS. 2013; 27:2375–84. https://doi.org/10.1097/QAD.0b013e328363bf7f [PubMed]
• 52. Horvath S, Levine AJ. HIV-1 infection accelerates age according to the epigenetic clock. J Infect Dis. 2015; 212:1563–73. https://doi.org/10.1093/infdis/jiv277 [PubMed]
• 53. Hsieh AY, Saberi S, Ajaykumar A, Hukezalie K, Gadawski I, Sattha B, Côté HC. Optimization of a relative telomere length assay by monochromatic multiplex real-time quantitative PCR on the LightCycler 480: sources of variability and quality control considerations. J Mol Diagn. 2016; 18:425–37. https://doi.org/10.1016/j.jmoldx.2016.01.004 [PubMed]
• 54. Hsieh AY, Budd M, Deng D, Gadawska I, Côté HC. A monochrome multiplex real-time quantitative PCR assay for the measurement of mitochondrial DNA content. J Mol Diagn. 2018; 20:612–20. https://doi.org/10.1016/j.jmoldx.2018.05.001 [PubMed]
• 55. Urata M, Koga-Wada Y, Kayamori Y, Kang D. Platelet contamination causes large variation as well as overestimation of mitochondrial DNA content of peripheral blood mononuclear cells. Ann Clin Biochem. 2008; 45:513–14. https://doi.org/10.1258/acb.2008.008008 [PubMed]
• 56. Hurtado-Roca Y, Ledesma M, Gonzalez-Lazaro M, Moreno-Loshuertos R, Fernandez-Silva P, Enriquez JA, Laclaustra M. Adjusting MtDNA quantification in whole blood for peripheral blood platelet and leukocyte counts. PLoS One. 2016; 11:e0163770. https://doi.org/10.1371/journal.pone.0163770 [PubMed]
• 57. Budd MA, Calli K, Samson L, Bowes J, Hsieh AY, Forbes JC, Bitnun A, Singer J, Kakkar F, Alimenti A, Maan EJ, Lewis ME, Gentile C, et al. Blood mitochondrial DNA content in HIV-exposed uninfected children with autism spectrum disorder. Viruses. 2018; 10:77. https://doi.org/10.3390/v10020077 [PubMed]
• 58. Pavanello S, Hoxha M, Dioni L, Bertazzi PA, Snenghi R, Nalesso A, Ferrara SD, Montisci M, Baccarelli A. Shortened telomeres in individuals with abuse in alcohol consumption. Int J Cancer. 2011; 129:983–92. https://doi.org/10.1002/ijc.25999 [PubMed]
• 59. Dixit S, Whooley MA, Vittinghoff E, Roberts JD, Heckbert SR, Fitzpatrick AL, Lin J, Leung C, Mukamal KJ, Marcus GM. Alcohol consumption and leukocyte telomere length. Sci Rep. 2019; 9:1404. https://doi.org/10.1038/s41598-019-38904-0 [PubMed]