Cells are powered by internal structures called mitochondria which have their own DNA molecules. How many copies of mitochondrial DNA blood cells contain is one aspect of mitochondrial health and is considered to provide a good indication of an individual’s ability to convert glucose into energy. Consequently, changes in the amount of mitochondrial DNA in the blood are linked to conditions like diabetes and cancer, and have also been associated with aging and mortality.

A set of well-classified personality traits known as ‘the Big Five’ have also been shown to affect energy levels and the longevity of individuals. However, it remained unclear if there is a relationship between these characteristics and the number of copies of mitochondrial DNA in the blood.

To investigate, Oppong et al. used a specialized test to assess the personality traits of participants from two separate cohorts: Baltimore Longitudinal Study of Ageing and the SardiNIA Project. The genomic sequence of each person was then analyzed to calculate the amount of mitochondrial DNA in their blood, and their mortality was recorded based on whether they were alive or dead multiple years later.

Oppong et al. found that low levels of mitochondrial DNA were linked with high scores in neuroticism (a trait typically associated with anxiety, depression, and self-doubt). Further statistical tests revealed that mitochondrial DNA levels mediate the relationship between a person’s personality and their risk of death.

These findings suggest that personality traits impact the number of mitochondrial DNA molecules in a person’s blood, which, in turn, influences how long they are likely to live. However, further work is needed to find out what causes this effect.

Mitochondria are cellular organelles essential for the life of eukaryotic cells (Duchen, 2004). They produce adenosine 5'-triphosphate (ATP), the main energetic currency for biological function, by oxidative phosphorylation (OXPHOS) of a number of substrates (Attardi and Schatz, 1988). Mitochondria harbor their own circular DNA molecules (mtDNA), which lack histone proteins and bear genes lacking introns (Attardi and Schatz, 1988). The proximity of mtDNA to OXPHOS production and its lack of histone protection make it more susceptible to ROS damage, threatening healthy cellular function (Clay Montier et al., 2009). The number of copies of mtDNA (mtDNAcn) in tissues and blood leukocytes is considered an imperfect proxy measure of mitochondrial mass that may reflect energetic resilience or hematopoiesis (Picard, 2021).

Consequently, reduced mtDNAcn in peripheral blood has been associated with various health outcomes, including renal cell carcinoma (Xing et al., 2008), hypertension (Hägg et al., 2021), neurodegenerative disease (Yang et al., 2021), and diabetes-related conditions, including insulin sensitivity (Gianotti et al., 2008; Song et al., 2001), fasting plasma glucose level (Xu et al., 2012), and diabetic nephropathy (Al-Kafaji et al., 2018). High levels of mtDNAcn in tissue and the blood can also occur in conditions of stress and illness such as diabetes (Moore et al., 2018) and peripheral artery disease (McDermott et al., 2018), suggesting that in some situations of stress, mtDNAcn can be elevated to compensate for low cellular energy (Picard, 2021; Giordano et al., 2014) and stress adaptation (Picard and McEwen, 2018a). For major depression, however, the findings are mixed, with some studies reporting that mtDNAcn has a positive (Cai et al., 2015), negative (Kim et al., 2011), or null association with depression (de Sousa et al., 2014; He et al., 2014; Lindqvist et al., 2018; Verhoeven et al., 2018; Vyas et al., 2020).

Since specific personality traits affect individuals’ susceptibility to environmental stress (Ebstrup et al., 2011) and the development of pathology and premature death, we hypothesized that blood mtDNAcn is a biomarker of stress burden and might mediate the association between personality traits and excess mortality (Chapman et al., 2020). To test this hypothesis, we tested the relationship between personality and levels of whole-blood mtDNAcn estimated by whole-genome sequencing and whether mtDNAcn mediated the relation between personality and mortality. To ensure replicability and robustness, we tested these associations in two cohorts, one from the United States and the other from Italy.

To assess personality, we used participants’ profiles of the big five domains, including the six facets of each domain, measured with the Revised NEO Personality Inventory (NEO-PI-R), a personality measure validated extensively in multiple populations and languages (Costa and McCrae, 2008). Personality traits measured with the NEO-PI-R have been associated with stressors such as anxiety and depression (Hayward et al., 2013; Kotov et al., 2010), cognitive decline (Caselli et al., 2016; Luchetti et al., 2016), metabolic dysfunction (Jokela et al., 2014; Stephan et al., 2016), energy levels (Terracciano et al., 2013), and physical illness (Smith and MacKenzie, 2006). Facets of personality have also been associated with mortality risk (Chapman et al., 2020). Compared with behavioral pathways to mortality risk (Turiano et al., 2015), there has been less attention on the physiological mechanisms that explain the well-known association between personality and mortality risk. As mentioned above, lower mtDNAcn has been associated with both stressful situations and higher mortality (Mengel-From et al., 2014). We thus address the critical novel question by testing whether mtDNAcn mediates the association between personality traits and mortality risk.

The goal of this study was to investigate the association between blood mtDNAcn and personality traits and facets assessed by the NEO-PI-R in two epidemiological studies: the Baltimore Longitudinal Study of Aging (BLSA) (Kuo et al., 2020; Stone and Norris, 1966) and the SardiNIA study (Pilia et al., 2006). Using mediation analysis, we further tested the hypothesis that mtDNAcn is a biomarker in the biological process that connects personality characteristics with mortality.

An extensive literature has reported the association of blood mtDNAcn with various psychological outcomes such as major depression (Cai et al., 2015; Kim et al., 2011), autism (Giulivi et al., 2010), bipolar disorder (Chang et al., 2014), and stress (Bersani et al., 2016; Boeck et al., 2016; Tyrka et al., 2016). Our study adds new information to this literature by reporting an association between mtDNAcn and personality traits. Our results show that higher mtDNAcn is significantly associated with a lower level of Neuroticism and higher levels of Extraversion, Openness, Agreeableness, and Conscientiousness in study participants. We demonstrate the robustness of these associations by showing that the direction and size of the effects are robust to adjustment for potential confounders such as age, sex, platelet count, and WBC parameters and are replicated across two cohorts. Consistent with Neuroticism, participants with more depressive symptoms had lower mtDNAcn than those without depressive symptoms. Finally, we report an effect of personality on mortality and demonstrate that this effect is partially mediated via mtDNAcn.

mtDNAcn is a biomarker of energetic health and cellular aging (Picard and McEwen, 2018a). The mechanism that controls the levels of mtDNAcn in tissues, blood, and plasma is not fully understood, and it is unclear whether the change of blood mtDNAcn reflects mitochondrial biogenesis, immune health, and/or hematopoiesis in the bone marrow (Picard, 2021). Increased mtDNAcn have been shown to have a different and potentially opposite indication depending on the underlying health condition (Moore et al., 2018). In population-based cohorts such as those involved in this study, increased mtDNAcn is generally positively correlated with mitochondria mass and thus is generally associated with higher oxidative capacity. Having a large pool of well-functioning mitochondria can positively influence health and generate signals that contribute to stress adaptation (Picard and McEwen, 2018a). Consistent with this interpretation, we found that the mtDNAcn levels were higher in younger participants, and our primary findings indicated that individuals with higher mtDNAcn levels had lower Neuroticism and were at lower risk of depression and lower risk of mortality. In adverse health conditions, however, it should be noted that increased mtDNAcn may not correlate with function (Moore et al., 2018; Wei et al., 2001). Rather, it may indicate a compensatory upregulation of mtDNAcn (Picard, 2021), a phenomenon where mtDNAcn is increased to compensate for low-energy cellular environment due to poor mitochondrial energetics (Picard, 2021; Giordano et al., 2014).

Personality traits influence behaviors, emotional states, and how individuals deal with stress. For example, personality traits are associated with how individuals assess stressful life circumstances (Ebstrup et al., 2011) and what coping responses to the stressors (i.e., neuroendocrine responses, immune functioning and inflammation, cardiovascular responses) are elicited (Schneiderman et al., 2005). Given these associations with stress and health-related behaviors, it is not surprising that personality traits are also related to better health, function, and longevity (Hayward et al., 2013; Caselli et al., 2016; Smith and MacKenzie, 2006; Terracciano and Sutin, 2019; Bogg and Roberts, 2004; Sutin et al., 2019; Canada et al., 2021). We observed a positive association between mtDNAcn and personality traits that are also related to health-relevant habits and conditions, such as nonsmoking (Malouff et al., 2006), healthy diet and obesity (Jokela et al., 2013; Mõttus et al., 2013), exercise and physical function (Kekäläinen et al., 2020), and preventive medical screenings (Aschwanden et al., 2019). Thus, indirect pathways may exist to link personality and health-related behaviors to changes in mitochondrial biology. Our finding of an association between mtDNAcn and personality expands this broader literature on personality traits and health.

Reduced mtDNAcn may reflect reduced mitochondrial mass or loss of mtDNA due to mitochondrial dysfunction (Picard, 2021). Dysfunctional mitochondria that are not appropriately removed by mitophagy produce excess ROS, which causes oxidative stress. As a result of damage caused by oxidative stress, mitochondria release mitochondrial antigenic molecules called damage-associated molecular patterns (DAMPs) into the cytoplasm (Picard and McEwen, 2018a; Zampino et al., 2020). These released DAMPs contain mtDNA, which are unmethylated and thus act as antigenic fragments and elicit an innate immune response through toll-like receptors (Zhang et al., 2010). This triggers an inflammatory response through cytokine release (Picard and McEwen, 2018a; Zampino et al., 2020). Prolonged inflammation, when unresolved, can lead to damage to cells and tissues and has been associated with a number of chronic diseases, including cardiovascular diseases (Lopez-Candales et al., 2017), depression (Maydych, 2019), and neurodegeneration (Missiroli et al., 2020). Our study shows that low mtDNAcn is associated with higher levels of Neuroticism and facets of Neuroticism, including depression. Of note, data from the SardiNIA sample indicates that Neuroticism is associated with higher interleukin-6 (IL-6) and C-reactive protein (CRP) (Sutin et al., 2010). We observed a similar relationship between mtDNAcn and CES-D (depressive symptoms), and the effect becomes even more pronounced (almost doubling in effect size) when considering significant depressive symptoms (setting CES-D binary cutoff at 20 instead of at 16). Kim et al., 2011 report a similar negative association between mtDNAcn and depression. However, other studies like Cai et al., 2015 and Tyrka et al., 2016 report a positive association of mtDNAcn with major depression, and several studies have found null associations (de Sousa et al., 2014; He et al., 2014; Lindqvist et al., 2018; Verhoeven et al., 2018; Vyas et al., 2020). One explanation for these inconsistencies could be the heterogeneity of the depression phenotyping: the depression facet of the Neuroticism domain is a trait measure, and the CES-D measures symptoms, but neither is a diagnostic instrument for major depression. It should also be noted that although some past studies assayed for mtDNAcn from whole blood, they did not adjust for the effect of platelets, WBCs, and other leukocyte subgroups on mtDNAcn as we do in this study. mtDNAcn estimates differ among the various blood cell types (Rausser et al., 2021), and this significantly impacts whole-blood mtDNAcn estimates and should be adjusted for in association models (also see discussion below).

mtDNAcn and personality are both predictors of mortality (Chapman et al., 2020; Mengel-From et al., 2014; Ashar et al., 2015), thus making our finding of an association between the two even more intriguing. Understanding which variable affects the other in driving the effect on mortality is a significant step forward, but alternative models are similarly plausible. To test the hypothesis that mtDNAcn mediates the association between personality and mortality, we performed a causal mediation analysis. In this article, we provide the first evidence of mtDNAcn significantly mediating the association between personality (including three facets capturing positive psychosocial states [activity, self-discipline, and competence] and an emotional stressor [vulnerability]) and mortality. Advances in mitochondrial research have presented the mitochondrion as a target of stress (Picard and McEwen, 2018b), and more recently, as a potential biological mediator of the stress-disease cascade (Picard and McEwen, 2018a). Picard and McEwen, 2018a provide a conceptual model for mitochondrial stress pathophysiology, outlining how mitochondria interact with psychosocial factors and stressors to influence health and lifespan outcomes. The underlying mechanism for this mediation is still an area of active research and is not fully understood, but the significance of our results remains clear: mtDNAcn is one mechanism in the pathway that connects personality and mortality.

This study also shows that mtDNAcn was negatively associated with age and male biological sex, which is consistent with previous reports (Ding et al., 2015). Also, higher mtDNAcn was significantly associated with higher levels of platelet count and percentages of lymphocytes, eosinophils, monocytes, and basophils. However, higher mtDNAcn was associated with lower WBC count and lower neutrophil percentage. Neutrophils have relatively lower mtDNAcn compared to other leukocytes (Rausser et al., 2021), and they form the largest proportion of WBC, which may explain the negative association of WBC count with mtDNAcn. A similar negative association between mtDNAcn and WBC count was observed by Liu et al., 2020. This study and other recent publications Hurtado-Roca et al., 2016; Knez et al., 2016; Shim et al., 2020 have highlighted the importance of adjusting for WBC parameters and platelet count when testing the association between traits and mtDNAcn measured in whole blood.

The primary strength of this study is the use of two independent study cohorts, one in the United States and the other in Europe: the top associations not only had significant p-values in both studies but also had very similar estimated effect sizes. Furthermore, in both cohorts, the associations of the personality traits and mtDNAcn ranked high when put in a broader context of mtDNAcn associations with other traits. The consistency of the results with the previous literature also clearly demonstrates the reproducibility of this study. The way mtDNAcn is estimated using whole-genome sequence information from the two study cohorts is also seen as the gold standard going forward in this line of research (Filograna et al., 2021), with major mitochondria consortia such as TopMed (Liu et al., 2020) employing the same approach. A limitation of this study was the one-time assessment of mtDNAcn, which precluded a longitudinal analysis of changes in mtDNAcn. Personality was assessed only once in the SardiNIA cohort, but personality is relatively stable in adults over long periods (Costa and McCrae, 1988; Terracciano et al., 2006); therefore, not much information may be lost in a cross-sectional analysis of personality. Additionally, we note that the formula for mtDNA copy number calculates a weighted average (weights determined by the proportion of leukocyte populations) of mtDNA copy number per cell across all leukocytes; each cell has two copies of nuclear DNA that provide the reference for the calculation. However, this does not take into account the platelet content of the buffy coat. Because each platelet has about 1.6 copies of mtDNA (Hurtado-Roca et al., 2016; Urata et al., 2008) but does not contain nuclear DNA, the formula somewhat overestimates mtDNA copy number (Rausser et al., 2021). The platelet content of buffy coat may also vary, representing a limitation of this and other studies of mtDNA in blood DNA material (Picard, 2021). Because of this limitation, papers published on mtDNA copy number typically use both platelets count and WBC counts as covariates in any models testing the association between mtDNA copy number and quantitative traits. We also used this conventional approach in our analyses. Another drawback of this study is the difference in the distribution of age and mtDNAcn across the two cohorts. The differences in the average mtDNAcn between the two cohorts can be explained by the gap in the time at which sequencing was done; almost a decade passed between the time the two cohorts were sequenced. Despite these limitations, the associations between mtDNAcn and personality traits are clear and consistent across the two study cohorts.

In this study, we showed a significant association between mtDNAcn and neuroticism traits that were replicated in two independent cohorts. Other larger cohorts (e.g., UK Biobank) have also collected certain personality information (e.g., questions about neuroticism), as well as whole-genome sequencing data (which makes estimating mtDNAcn feasible). Therefore, trying to replicate our findings in such larger cohorts will be a natural extension of this study.

In conclusion, we provide the first evidence of an association between mtDNAcn and personality. These results may provide further evidence of the link between mtDNAcn and psychosocial stress and suggest that blood mtDNAcn may correlate with changes in mitochondrial biology under stress. These initial findings will require further exploration to reveal the potential causal pathways between personality and mtDNAcn. We also provide novel insights into the effect of personality on mortality by showing that this effect is mediated through mtDNAcn.

Researchers interested in using BLSA data (e.g., sequencing or phenotypic data) need to submit an analysis plan to a review committee organized by the National Institute on Aging (A detailed guideline for the submission of an analysis plan can be found at https://www.blsa.nih.gov/how-apply). Researchers will get the access once the proposed analysis plans are reviewed and approved. Aggregated genetic and phenotypic data from the SardiNIA study are deposited in the European Genome-phenome Archive (EGA) (https://ega-archive.org/studies/phs000338). In accord with European Union regulations, disaggregated data, including sequence/genotyping data of individuals, cannot be made public, but investigators can request specific analyses by sending a one-page proposal description to Dr. F. Cucca (fcucca@uniss.it), the P.I. of the study. After screening by an internal committee, the requested analyses are customarily done by staff of IRGB and the results provided to the requestor. The R code for the analyses performed in this paper is provided as a Supplementary file (source code file: R code for the analysis on mtDNAcn and personality).

1. 1. Al-Kafaji G
   2. Aljadaan A
   3. Kamal A
   4. Bakhiet M

   (2018) Peripheral blood mitochondrial DNA copy number as a novel potential biomarker for diabetic nephropathy in type 2 diabetes patients

   Experimental and Therapeutic Medicine 16:1483–1492.

   https://doi.org/10.3892/etm.2018.6319

   • PubMed
   • Google Scholar
2. 1. Aschwanden D
   2. Gerend MA
   3. Luchetti M
   4. Stephan Y
   5. Sutin AR
   6. Terracciano A

   (2019) Personality traits and preventive cancer screenings in the health retirement study

   Preventive Medicine 126:105763.

   https://doi.org/10.1016/j.ypmed.2019.105763

   • PubMed
   • Google Scholar
3. 1. Ashar FN
   2. Moes A
   3. Moore AZ
   4. Grove ML
   5. Chaves PHM
   6. Coresh J
   7. Newman AB
   8. Matteini AM
   9. Bandeen-Roche K
   10. Boerwinkle E
   11. Walston JD
   12. Arking DE

   (2015) Association of mitochondrial DNA levels with frailty and all-cause mortality

   Journal of Molecular Medicine 93:177–186.

   https://doi.org/10.1007/s00109-014-1233-3

   • PubMed
   • Google Scholar
4. 1. Attardi G
   2. Schatz G

   (1988) Biogenesis of mitochondria

   Annual Review of Cell Biology 4:289–333.

   https://doi.org/10.1146/annurev.cb.04.110188.001445

   • PubMed
   • Google Scholar
5. 1. Beekman AT
   2. Deeg DJ
   3. Van Limbeek J
   4. Braam AW
   5. De Vries MZ
   6. Van Tilburg W

   (1997) Criterion validity of the center for epidemiologic studies depression scale (CES-D): results from a community-based sample of older subjects in the Netherlands

   Psychological Medicine 27:231–235.

   https://doi.org/10.1017/s0033291796003510

   • PubMed
   • Google Scholar
6. 1. Bersani FS
   2. Morley C
   3. Lindqvist D
   4. Epel ES
   5. Picard M
   6. Yehuda R
   7. Flory J
   8. Bierer LM
   9. Makotkine I
   10. Abu-Amara D
   11. Coy M
   12. Reus VI
   13. Lin J
   14. Blackburn EH
   15. Marmar C
   16. Wolkowitz OM
   17. Mellon SH

   (2016) Mitochondrial DNA copy number is reduced in male combat veterans with PTSD

   Progress in Neuro-Psychopharmacology & Biological Psychiatry 64:10–17.

   https://doi.org/10.1016/j.pnpbp.2015.06.012

   • PubMed
   • Google Scholar
7. 1. Boeck C
   2. Koenig AM
   3. Schury K
   4. Geiger ML
   5. Karabatsiakis A
   6. Wilker S
   7. Waller C
   8. Gündel H
   9. Fegert JM
   10. Calzia E
   11. Kolassa I-T

   (2016) Inflammation in adult women with a history of child maltreatment: the involvement of mitochondrial alterations and oxidative stress

   Mitochondrion 30:197–207.

   https://doi.org/10.1016/j.mito.2016.08.006

   • PubMed
   • Google Scholar
8. 1. Bogg T
   2. Roberts BW

   (2004) Conscientiousness and health-related behaviors: a meta-analysis of the leading behavioral contributors to mortality

   Psychological Bulletin 130:887–919.

   https://doi.org/10.1037/0033-2909.130.6.887

   • PubMed
   • Google Scholar
9. 1. Cai N
   2. Chang S
   3. Li Y
   4. Li Q
   5. Hu J
   6. Liang J
   7. Song L
   8. Kretzschmar W
   9. Gan X
   10. Nicod J
   11. Rivera M
   12. Deng H
   13. Du B
   14. Li K
   15. Sang W
   16. Gao J
   17. Gao S
   18. Ha B
   19. Ho H-Y
   20. Hu C
   21. Hu J
   22. Hu Z
   23. Huang G
   24. Jiang G
   25. Jiang T
   26. Jin W
   27. Li G
   28. Li K
   29. Li Y
   30. Li Y
   31. Li Y
   32. Lin Y-T
   33. Liu L
   34. Liu T
   35. Liu Y
   36. Liu Y
   37. Lu Y
   38. Lv L
   39. Meng H
   40. Qian P
   41. Sang H
   42. Shen J
   43. Shi J
   44. Sun J
   45. Tao M
   46. Wang G
   47. Wang G
   48. Wang J
   49. Wang L
   50. Wang X
   51. Wang X
   52. Yang H
   53. Yang L
   54. Yin Y
   55. Zhang J
   56. Zhang K
   57. Sun N
   58. Zhang W
   59. Zhang X
   60. Zhang Z
   61. Zhong H
   62. Breen G
   63. Wang J
   64. Marchini J
   65. Chen Y
   66. Xu Q
   67. Xu X
   68. Mott R
   69. Huang G-J
   70. Kendler K
   71. Flint J

   (2015) Molecular signatures of major depression

   Current Biology 25:1146–1156.

   https://doi.org/10.1016/j.cub.2015.03.008

   • PubMed
   • Google Scholar
10. 1. Canada B
    2. Stephan Y
    3. Fundenberger H
    4. Sutin AR
    5. Terracciano A

    (2021) Cross-Sectional and prospective association between personality traits and IADL/ADL limitations

    Psychology and Aging 36:309–321.

    https://doi.org/10.1037/pag0000502

    • PubMed
    • Google Scholar
11. 1. Caselli RJ
    2. Dueck AC
    3. Locke DEC
    4. Henslin BR
    5. Johnson TA
    6. Woodruff BK
    7. Hoffman-Snyder C
    8. Geda YE

    (2016) Impact of personality on cognitive aging: a prospective cohort study

    Journal of the International Neuropsychological Society 22:765–776.

    https://doi.org/10.1017/S1355617716000527

    • PubMed
    • Google Scholar
12. 1. Chang CC
    2. Jou SH
    3. Lin TT
    4. Liu CS

    (2014) Mitochondrial DNA variation and increased oxidative damage in euthymic patients with bipolar disorder

    Psychiatry and Clinical Neurosciences 68:551–557.

    https://doi.org/10.1111/pcn.12163

    • PubMed
    • Google Scholar
13. 1. Chapman BP
    2. Elliot A
    3. Sutin A
    4. Terraciano A
    5. Zelinski E
    6. Schaie W
    7. Willis S
    8. Hofer S

    (2020) Mortality risk associated with personality facets of the big five and interpersonal circumplex across three aging cohorts

    Psychosomatic Medicine 82:64–73.

    https://doi.org/10.1097/PSY.0000000000000756

    • PubMed
    • Google Scholar
14. 1. Chia R
    2. Sabir MS
    3. Bandres-Ciga S
    4. Saez-Atienzar S
    5. Reynolds RH
    6. Gustavsson E
    7. Walton RL
    8. Ahmed S
    9. Viollet C
    10. Ding J
    11. Makarious MB
    12. Diez-Fairen M
    13. Portley MK
    14. Shah Z
    15. Abramzon Y
    16. Hernandez DG
    17. Blauwendraat C
    18. Stone DJ
    19. Eicher J
    20. Parkkinen L
    21. Ansorge O
    22. Clark L
    23. Honig LS
    24. Marder K
    25. Lemstra A
    26. St George-Hyslop P
    27. Londos E
    28. Morgan K
    29. Lashley T
    30. Warner TT
    31. Jaunmuktane Z
    32. Galasko D
    33. Santana I
    34. Tienari PJ
    35. Myllykangas L
    36. Oinas M
    37. Cairns NJ
    38. Morris JC
    39. Halliday GM
    40. Van Deerlin VM
    41. Trojanowski JQ
    42. Grassano M
    43. Calvo A
    44. Mora G
    45. Canosa A
    46. Floris G
    47. Bohannan RC
    48. Brett F
    49. Gan-Or Z
    50. Geiger JT
    51. Moore A
    52. May P
    53. Krüger R
    54. Goldstein DS
    55. Lopez G
    56. Tayebi N
    57. Sidransky E
    58. American Genome Center
    59. Norcliffe-Kaufmann L
    60. Palma J-A
    61. Kaufmann H
    62. Shakkottai VG
    63. Perkins M
    64. Newell KL
    65. Gasser T
    66. Schulte C
    67. Landi F
    68. Salvi E
    69. Cusi D
    70. Masliah E
    71. Kim RC
    72. Caraway CA
    73. Monuki ES
    74. Brunetti M
    75. Dawson TM
    76. Rosenthal LS
    77. Albert MS
    78. Pletnikova O
    79. Troncoso JC
    80. Flanagan ME
    81. Mao Q
    82. Bigio EH
    83. Rodríguez-Rodríguez E
    84. Infante J
    85. Lage C
    86. González-Aramburu I
    87. Sanchez-Juan P
    88. Ghetti B
    89. Keith J
    90. Black SE
    91. Masellis M
    92. Rogaeva E
    93. Duyckaerts C
    94. Brice A
    95. Lesage S
    96. Xiromerisiou G
    97. Barrett MJ
    98. Tilley BS
    99. Gentleman S
    100. Logroscino G
    101. Serrano GE
    102. Beach TG
    103. McKeith IG
    104. Thomas AJ
    105. Attems J
    106. Morris CM
    107. Palmer L
    108. Love S
    109. Troakes C
    110. Al-Sarraj S
    111. Hodges AK
    112. Aarsland D
    113. Klein G
    114. Kaiser SM
    115. Woltjer R
    116. Pastor P
    117. Bekris LM
    118. Leverenz JB
    119. Besser LM
    120. Kuzma A
    121. Renton AE
    122. Goate A
    123. Bennett DA
    124. Scherzer CR
    125. Morris HR
    126. Ferrari R
    127. Albani D
    128. Pickering-Brown S
    129. Faber K
    130. Kukull WA
    131. Morenas-Rodriguez E
    132. Lleó A
    133. Fortea J
    134. Alcolea D
    135. Clarimon J
    136. Nalls MA
    137. Ferrucci L
    138. Resnick SM
    139. Tanaka T
    140. Foroud TM
    141. Graff-Radford NR
    142. Wszolek ZK
    143. Ferman T
    144. Boeve BF
    145. Hardy JA
    146. Topol EJ
    147. Torkamani A
    148. Singleton AB
    149. Ryten M
    150. Dickson DW
    151. Chiò A
    152. Ross OA
    153. Gibbs JR
    154. Dalgard CL
    155. Traynor BJ
    156. Scholz SW

    (2021) Genome sequencing analysis identifies new loci associated with Lewy body dementia and provides insights into its genetic architecture

    Nature Genetics 53:294–303.

    https://doi.org/10.1038/s41588-021-00785-3

    • PubMed
    • Google Scholar
15. 1. Clay Montier LL
    2. Deng JJ
    3. Bai Y

    (2009) Number matters: control of mammalian mitochondrial DNA copy number

    Journal of Genetics and Genomics = Yi Chuan Xue Bao 36:125–131.

    https://doi.org/10.1016/S1673-8527(08)60099-5

    • PubMed
    • Google Scholar
16. 1. Costa PT
    2. McCrae RR

    (1988) Personality in adulthood: a six-year longitudinal study of self-reports and spouse ratings on the neo personality inventory

    Journal of Personality and Social Psychology 54:853–863.

    https://doi.org/10.1037//0022-3514.54.5.853

    • PubMed
    • Google Scholar
17. 1. Costa PT
    2. Terracciano A
    3. Uda M
    4. Vacca L
    5. Mameli C
    6. Pilia G
    7. Zonderman AB
    8. Lakatta E
    9. Schlessinger D
    10. McCrae RR

    (2007) Personality traits in sardinia: testing founder population effects on trait means and variances

    Behavior Genetics 37:376–387.

    https://doi.org/10.1007/s10519-006-9103-6

    • PubMed
    • Google Scholar
18. 1. Costa P
    2. McCrae R

    (2008)

    The revised NEO personality inventory (NEO-PI-R)

    SAGE 2:179–198.

    • Google Scholar
19. 1. Delitala AP
    2. Terracciano A
    3. Fiorillo E
    4. Orrù V
    5. Schlessinger D
    6. Cucca F

    (2016) Depressive symptoms, thyroid hormone and autoimmunity in a population-based cohort from Sardinia

    Journal of Affective Disorders 191:82–87.

    https://doi.org/10.1016/j.jad.2015.11.019

    • PubMed
    • Google Scholar
20. 1. de Sousa RT
    2. Uno M
    3. Zanetti MV
    4. Shinjo SMO
    5. Busatto GF
    6. Gattaz WF
    7. Marie SKN
    8. Machado-Vieira R

    (2014) Leukocyte mitochondrial DNA copy number in bipolar disorder

    Progress in Neuro-Psychopharmacology & Biological Psychiatry 48:32–35.

    https://doi.org/10.1016/j.pnpbp.2013.09.002

    • PubMed
    • Google Scholar
21. 1. Ding J
    2. Sidore C
    3. Butler TJ
    4. Wing MK
    5. Qian Y
    6. Meirelles O
    7. Busonero F
    8. Tsoi LC
    9. Maschio A
    10. Angius A
    11. Kang HM
    12. Nagaraja R
    13. Cucca F
    14. Abecasis GR
    15. Schlessinger D

    (2015) Assessing mitochondrial DNA variation and copy number in lymphocytes of ~2,000 Sardinians using tailored sequencing analysis tools

    PLOS Genetics 11:e1005306.

    https://doi.org/10.1371/journal.pgen.1005306

    • PubMed
    • Google Scholar
22. 1. Duchen MR

    (2004) Mitochondria in health and disease: perspectives on a new mitochondrial biology

    Molecular Aspects of Medicine 25:365–451.

    https://doi.org/10.1016/j.mam.2004.03.001

    • PubMed
    • Google Scholar
23. 1. Ebstrup JF
    2. Eplov LF
    3. Pisinger C
    4. Jørgensen T

    (2011) Association between the five factor personality traits and perceived stress: is the effect mediated by general self-efficacy?

    Anxiety, Stress, and Coping 24:407–419.

    https://doi.org/10.1080/10615806.2010.540012

    • PubMed
    • Google Scholar
24. 1. Filograna R
    2. Mennuni M
    3. Alsina D
    4. Larsson NG

    (2021) Mitochondrial DNA copy number in human disease: the more the better?

    FEBS Letters 595:976–1002.

    https://doi.org/10.1002/1873-3468.14021

    • PubMed
    • Google Scholar
25. 1. Gianotti TF
    2. Sookoian S
    3. Dieuzeide G
    4. García SI
    5. Gemma C
    6. González CD
    7. Pirola CJ

    (2008) A decreased mitochondrial DNA content is related to insulin resistance in adolescents

    Obesity 16:1591–1595.

    https://doi.org/10.1038/oby.2008.253

    • PubMed
    • Google Scholar
26. 1. Giordano C
    2. Iommarini L
    3. Giordano L
    4. Maresca A
    5. Pisano A
    6. Valentino ML
    7. Caporali L
    8. Liguori R
    9. Deceglie S
    10. Roberti M
    11. Fanelli F
    12. Fracasso F
    13. Ross-Cisneros FN
    14. D’Adamo P
    15. Hudson G
    16. Pyle A
    17. Yu-Wai-Man P
    18. Chinnery PF
    19. Zeviani M
    20. Salomao SR
    21. Berezovsky A
    22. Belfort R
    23. Ventura DF
    24. Moraes M
    25. Moraes Filho M
    26. Barboni P
    27. Sadun F
    28. De Negri A
    29. Sadun AA
    30. Tancredi A
    31. Mancini M
    32. d’Amati G
    33. Loguercio Polosa P
    34. Cantatore P
    35. Carelli V

    (2014) Efficient mitochondrial biogenesis drives incomplete penetrance in leber’s hereditary optic neuropathy

    Brain 137:335–353.

    https://doi.org/10.1093/brain/awt343

    • PubMed
    • Google Scholar
27. 1. Giulivi C
    2. Zhang Y-F
    3. Omanska-Klusek A
    4. Ross-Inta C
    5. Wong S
    6. Hertz-Picciotto I
    7. Tassone F
    8. Pessah IN

    (2010) Mitochondrial dysfunction in autism

    JAMA 304:2389–2396.

    https://doi.org/10.1001/jama.2010.1706

    • PubMed
    • Google Scholar
28. 1. Hägg S
    2. Jylhävä J
    3. Wang Y
    4. Czene K
    5. Grassmann F

    (2021) Deciphering the genetic and epidemiological landscape of mitochondrial DNA abundance

    Human Genetics 140:849–861.

    https://doi.org/10.1007/s00439-020-02249-w

    • PubMed
    • Google Scholar
29. 1. Hayward RD
    2. Taylor WD
    3. Smoski MJ
    4. Steffens DC
    5. Payne ME

    (2013) Association of five-factor model personality domains and facets with presence, onset, and treatment outcomes of major depression in older adults

    The American Journal of Geriatric Psychiatry 21:88–96.

    https://doi.org/10.1016/j.jagp.2012.11.012

    • PubMed
    • Google Scholar
30. 1. He Y
    2. Tang J
    3. Li Z
    4. Li H
    5. Liao Y
    6. Tang Y
    7. Tan L
    8. Chen J
    9. Xia K
    10. Chen X

    (2014) Leukocyte mitochondrial DNA copy number in blood is not associated with major depressive disorder in young adults

    PLOS ONE 9:e96869.

    https://doi.org/10.1371/journal.pone.0096869

    • PubMed
    • Google Scholar
31. 1. Hurtado-Roca Y
    2. Ledesma M
    3. Gonzalez-Lazaro M
    4. Moreno-Loshuertos R
    5. Fernandez-Silva P
    6. Enriquez JA
    7. Laclaustra M

    (2016) Adjusting mtDNA quantification in whole blood for peripheral blood platelet and leukocyte counts

    PLOS ONE 11:e0163770.

    https://doi.org/10.1371/journal.pone.0163770

    • PubMed
    • Google Scholar
32. 1. Islam M
    2. Mazumder M
    3. Schwabe-Warf D
    4. Stephan Y
    5. Sutin AR
    6. Terracciano A

    (2019) Personality changes with dementia from the informant perspective: new data and meta-analysis

    Journal of the American Medical Directors Association 20:131–137.

    https://doi.org/10.1016/j.jamda.2018.11.004

    • PubMed
    • Google Scholar
33. 1. Jokela M
    2. Hintsanen M
    3. Hakulinen C
    4. Batty GD
    5. Nabi H
    6. Singh-Manoux A
    7. Kivimäki M

    (2013) Association of personality with the development and persistence of obesity: a meta-analysis based on individual-participant data

    Obesity Reviews 14:315–323.

    https://doi.org/10.1111/obr.12007

    • PubMed
    • Google Scholar
34. 1. Jokela M
    2. Elovainio M
    3. Nyberg ST
    4. Tabák AG
    5. Hintsa T
    6. Batty GD
    7. Kivimäki M

    (2014) Personality and risk of diabetes in adults: pooled analysis of 5 cohort studies

    Health Psychology 33:1618–1621.

    https://doi.org/10.1037/hea0000003

    • PubMed
    • Google Scholar
35. 1. Kekäläinen T
    2. Terracciano A
    3. Sipilä S
    4. Kokko K

    (2020) Personality traits and physical functioning: a cross-sectional multimethod facet-level analysis

    European Review of Aging and Physical Activity 17:20.

    https://doi.org/10.1186/s11556-020-00251-9

    • PubMed
    • Google Scholar
36. 1. Kim MY
    2. Lee JW
    3. Kang HC
    4. Kim E
    5. Lee DC

    (2011) Leukocyte mitochondrial DNA (mtdna) content is associated with depression in old women

    Archives of Gerontology and Geriatrics 53:e218–e221.

    https://doi.org/10.1016/j.archger.2010.11.019

    • PubMed
    • Google Scholar
37. 1. Knez J
    2. Winckelmans E
    3. Plusquin M
    4. Thijs L
    5. Cauwenberghs N
    6. Gu Y
    7. Staessen JA
    8. Nawrot TS
    9. Kuznetsova T

    (2016) Correlates of peripheral blood mitochondrial DNA content in a general population

    American Journal of Epidemiology 183:138–146.

    https://doi.org/10.1093/aje/kwv175

    • PubMed
    • Google Scholar
38. 1. Kotov R
    2. Gamez W
    3. Schmidt F
    4. Watson D

    (2010) Linking “ big ” personality traits to anxiety, depressive, and substance use disorders: a meta-analysis

    Psychological Bulletin 136:768–821.

    https://doi.org/10.1037/a0020327

    • PubMed
    • Google Scholar
39. 1. Kuo P-L
    2. Schrack JA
    3. Shardell MD
    4. Levine M
    5. Moore AZ
    6. An Y
    7. Elango P
    8. Karikkineth A
    9. Tanaka T
    10. de Cabo R
    11. Zukley LM
    12. AlGhatrif M
    13. Chia CW
    14. Simonsick EM
    15. Egan JM
    16. Resnick SM
    17. Ferrucci L

    (2020) A roadmap to build a phenotypic metric of ageing: insights from the Baltimore longitudinal study of aging

    Journal of Internal Medicine 287:373–394.

    https://doi.org/10.1111/joim.13024

    • PubMed
    • Google Scholar
40. 1. Li H
    2. Handsaker B
    3. Wysoker A
    4. Fennell T
    5. Ruan J
    6. Homer N
    7. Marth G
    8. Abecasis G
    9. Durbin R
    10. 1000 Genome Project Data Processing Subgroup

    (2009) The sequence alignment/map format and samtools

    Bioinformatics 25:2078–2079.

    https://doi.org/10.1093/bioinformatics/btp352

    • PubMed
    • Google Scholar
41. 1. Lindqvist D
    2. Wolkowitz OM
    3. Picard M
    4. Ohlsson L
    5. Bersani FS
    6. Fernström J
    7. Westrin Å
    8. Hough CM
    9. Lin J
    10. Reus VI
    11. Epel ES
    12. Mellon SH

    (2018) Circulating cell-free mitochondrial DNA, but not leukocyte mitochondrial DNA copy number, is elevated in major depressive disorder

    Neuropsychopharmacology 43:1557–1564.

    https://doi.org/10.1038/s41386-017-0001-9

    • PubMed
    • Google Scholar
42. Preprint

    1. Liu X
    2. Longchamps RJ
    3. Wiggins K
    4. Raffield LM
    5. Bielak LF
    6. Zhao W
    7. Pitsillides A
    8. Blackwell T
    9. Yao J
    10. Guo X
    11. Kurniansyah N
    12. Thyagarajan B
    13. Pankratz N
    14. Rich SS
    15. Taylor KD
    16. Peyser PA
    17. Heckbert SR
    18. Seshadri S
    19. Cupples LA
    20. Boerwinkle E
    21. Grove ML
    22. Larson N
    23. Smith JA
    24. Vasan RS
    25. Sofer T
    26. Fitzpatrick AL
    27. Fornage M
    28. Ding J
    29. Correa A
    30. Abecasis G
    31. Psaty BM
    32. Wilson JG
    33. Levy D
    34. Rotter JI
    35. Bis JC
    36. Satizabal CL
    37. Arking DE
    38. Liu C

    (2020) Association of Mitochondrial DNA Copy Number with Cardiometabolic Diseases in a Large Cross-Sectional Study of Multiple Ancestries

    bioRxiv.

    https://doi.org/10.1101/2020.04.20.20016337

    • Google Scholar
43. 1. Lopez-Candales A
    2. Hernández Burgos PM
    3. Hernandez-Suarez DF
    4. Harris D

    (2017)

    Linking chronic inflammation with cardiovascular disease: from normal aging to the metabolic syndrome

    Journal of Nature and Science 3:e341.

    • PubMed
    • Google Scholar
44. 1. Luchetti M
    2. Terracciano A
    3. Stephan Y
    4. Sutin AR

    (2016) Personality and cognitive decline in older adults: data from a longitudinal sample and meta-analysis

    The Journals of Gerontology. Series B, Psychological Sciences and Social Sciences 71:591–601.

    https://doi.org/10.1093/geronb/gbu184

    • PubMed
    • Google Scholar
45. 1. Malouff JM
    2. Thorsteinsson EB
    3. Schutte NS

    (2006) The five-factor model of personality and smoking: a meta-analysis

    Journal of Drug Education 36:47–58.

    https://doi.org/10.2190/9EP8-17P8-EKG7-66AD

    • PubMed
    • Google Scholar
46. 1. Maydych V

    (2019) The interplay between stress, inflammation, and emotional attention: relevance for depression

    Frontiers in Neuroscience 13:384.

    https://doi.org/10.3389/fnins.2019.00384

    • PubMed
    • Google Scholar
47. 1. McCrae RR
    2. Terracciano A

    (2005)

    Personality profiles of cultures project, universal features of personality traits from the observer’s perspective: data from 50 cultures

    Journal of Personality and Social Psychology 88:547–561.

    • PubMed
    • Google Scholar
48. 1. McDermott MM
    2. Peterson CA
    3. Sufit R
    4. Ferrucci L
    5. Guralnik JM
    6. Kibbe MR
    7. Polonsky TS
    8. Tian L
    9. Criqui MH
    10. Zhao L
    11. Stein JH
    12. Li L
    13. Leeuwenburgh C

    (2018) Peripheral artery disease, calf skeletal muscle mitochondrial DNA copy number, and functional performance

    Vascular Medicine 23:340–348.

    https://doi.org/10.1177/1358863X18765667

    • PubMed
    • Google Scholar
49. 1. Mengel-From J
    2. Thinggaard M
    3. Dalgård C
    4. Kyvik KO
    5. Christensen K
    6. Christiansen L

    (2014) Mitochondrial DNA copy number in peripheral blood cells declines with age and is associated with general health among elderly

    Human Genetics 133:1149–1159.

    https://doi.org/10.1007/s00439-014-1458-9

    • PubMed
    • Google Scholar
50. 1. Milaneschi Y
    2. Bandinelli S
    3. Penninx BW
    4. Vogelzangs N
    5. Corsi AM
    6. Lauretani F
    7. Kisialiou A
    8. Vazzana R
    9. Terracciano A
    10. Guralnik JM
    11. Ferrucci L

    (2011) Depressive symptoms and inflammation increase in a prospective study of older adults: a protective effect of a healthy (mediterranean-style) diet

    Molecular Psychiatry 16:589–590.

    https://doi.org/10.1038/mp.2010.113

    • PubMed
    • Google Scholar
51. 1. Missiroli S
    2. Genovese I
    3. Perrone M
    4. Vezzani B
    5. Vitto VAM
    6. Giorgi C

    (2020) The role of mitochondria in inflammation: from cancer to neurodegenerative disorders

    Journal of Clinical Medicine 9:E740.

    https://doi.org/10.3390/jcm9030740

    • PubMed
    • Google Scholar
52. 1. Moore AZ
    2. Ding J
    3. Tuke MA
    4. Wood AR
    5. Bandinelli S
    6. Frayling TM
    7. Ferrucci L

    (2018) Influence of cell distribution and diabetes status on the association between mitochondrial DNA copy number and aging phenotypes in the inchianti study

    Aging Cell 17:12683.

    https://doi.org/10.1111/acel.12683

    • PubMed
    • Google Scholar
53. 1. Mõttus R
    2. McNeill G
    3. Jia X
    4. Craig LCA
    5. Starr JM
    6. Deary IJ

    (2013) The associations between personality, diet and body mass index in older people

    Health Psychology 32:353–360.

    https://doi.org/10.1037/a0025537

    • PubMed
    • Google Scholar
54. 1. Penninx BW
    2. Guralnik JM
    3. Ferrucci L
    4. Simonsick EM
    5. Deeg DJ
    6. Wallace RB

    (1998) Depressive symptoms and physical decline in community-dwelling older persons

    JAMA 279:1720–1726.

    https://doi.org/10.1001/jama.279.21.1720

    • PubMed
    • Google Scholar
55. 1. Picard M
    2. McEwen BS

    (2018a) Psychological stress and mitochondria: a conceptual framework

    Psychosomatic Medicine 80:126–140.

    https://doi.org/10.1097/PSY.0000000000000544

    • PubMed
    • Google Scholar
56. 1. Picard M
    2. McEwen BS

    (2018b) Psychological stress and mitochondria: a systematic review

    Psychosomatic Medicine 80:141–153.

    https://doi.org/10.1097/PSY.0000000000000545

    • PubMed
    • Google Scholar
57. 1. Picard M

    (2021) Blood mitochondrial DNA copy number: what are we counting?

    Mitochondrion 60:1–11.

    https://doi.org/10.1016/j.mito.2021.06.010

    • PubMed
    • Google Scholar
58. 1. Pilia G
    2. Chen W-M
    3. Scuteri A
    4. Orrú M
    5. Albai G
    6. Dei M
    7. Lai S
    8. Usala G
    9. Lai M
    10. Loi P
    11. Mameli C
    12. Vacca L
    13. Deiana M
    14. Olla N
    15. Masala M
    16. Cao A
    17. Najjar SS
    18. Terracciano A
    19. Nedorezov T
    20. Sharov A
    21. Zonderman AB
    22. Abecasis GR
    23. Costa P
    24. Lakatta E
    25. Schlessinger D

    (2006) Heritability of cardiovascular and personality traits in 6,148 Sardinians

    PLOS Genetics 2:e132.

    https://doi.org/10.1371/journal.pgen.0020132

    • PubMed
    • Google Scholar
59. 1. Qian Y
    2. Butler TJ
    3. Opsahl-Ong K
    4. Giroux NS
    5. Sidore C
    6. Nagaraja R
    7. Cucca F
    8. Ferrucci L
    9. Abecasis GR
    10. Schlessinger D
    11. Ding J

    (2017) FastMitoCalc: an ultra-fast program to estimate mitochondrial DNA copy number from whole-genome sequences

    Bioinformatics 33:1399–1401.

    https://doi.org/10.1093/bioinformatics/btw835

    • PubMed
    • Google Scholar
60. 1. Radloff LS

    (1977) The CES-D scale: A self-report depression scale for research in the general population

    SAGE 1:100306.

    https://doi.org/10.1177/014662167700100306

    • Google Scholar
61. 1. Rausser S
    2. Trumpff C
    3. McGill MA
    4. Junker A
    5. Wang W
    6. Ho S-H
    7. Mitchell A
    8. Karan KR
    9. Monk C
    10. Segerstrom SC
    11. Reed RG
    12. Picard M

    (2021) Mitochondrial phenotypes in purified human immune cell subtypes and cell mixtures

    eLife 10:e70899.

    https://doi.org/10.7554/eLife.70899

    • PubMed
    • Google Scholar
62. 1. Schneiderman N
    2. Ironson G
    3. Siegel SD

    (2005) Stress and health: psychological, behavioral, and biological determinants

    Annual Review of Clinical Psychology 1:607–628.

    https://doi.org/10.1146/annurev.clinpsy.1.102803.144141

    • PubMed
    • Google Scholar
63. 1. Shim HB
    2. Arshad O
    3. Gadawska I
    4. Côté HCF
    5. Hsieh AYY

    (2020) Platelet mtDNA content and leukocyte count influence whole blood mtDNA content

    Mitochondrion 52:108–114.

    https://doi.org/10.1016/j.mito.2020.03.001

    • PubMed
    • Google Scholar
64. 1. Smith TW
    2. MacKenzie J

    (2006) Personality and risk of physical illness

    Annual Review of Clinical Psychology 2:435–467.

    https://doi.org/10.1146/annurev.clinpsy.2.022305.095257

    • PubMed
    • Google Scholar
65. 1. Song J
    2. Oh JY
    3. Sung YA
    4. Pak YK
    5. Park KS
    6. Lee HK

    (2001) Peripheral blood mitochondrial DNA content is related to insulin sensitivity in offspring of type 2 diabetic patients

    Diabetes Care 24:865–869.

    https://doi.org/10.2337/diacare.24.5.865

    • PubMed
    • Google Scholar
66. 1. Stephan Y
    2. Sutin AR
    3. Luchetti M
    4. Terracciano A

    (2016) Allostatic load and personality: a 4-year longitudinal study

    Psychosomatic Medicine 78:302–310.

    https://doi.org/10.1097/PSY.0000000000000281

    • PubMed
    • Google Scholar
67. 1. Stone JL
    2. Norris AH

    (1966) Activities and attitudes of participants in the Baltimore longitudinal study

    Journal of Gerontology 21:575–580.

    https://doi.org/10.1093/geronj/21.4.575

    • PubMed
    • Google Scholar
68. 1. Sutin AR
    2. Terracciano A
    3. Deiana B
    4. Naitza S
    5. Ferrucci L
    6. Uda M
    7. Schlessinger D
    8. Costa PT Jr

    (2010) High neuroticism and low conscientiousness are associated with interleukin-6

    Psychological Medicine 40:1485–1493.

    https://doi.org/10.1017/S0033291709992029

    • PubMed
    • Google Scholar
69. 1. Sutin AR
    2. Stephan Y
    3. Terracciano A

    (2019) Personality and metabolic dysfunction in young adulthood: a cross-sectional study

    Journal of Health Psychology 24:495–501.

    https://doi.org/10.1177/1359105316677294

    • PubMed
    • Google Scholar
70. 1. Terracciano A

    (2003) The Italian version of the neo PI-R: conceptual and empirical support for the use of targeted rotation

    Personality and Individual Differences 35:1859–1872.

    https://doi.org/10.1016/S0191-8869(03)00035-7

    • PubMed
    • Google Scholar
71. 1. Terracciano A
    2. Costa PT
    3. McCrae RR

    (2006) Personality plasticity after age 30

    Personality & Social Psychology Bulletin 32:999–1009.

    https://doi.org/10.1177/0146167206288599

    • PubMed
    • Google Scholar
72. 1. Terracciano A
    2. Schrack JA
    3. Sutin AR
    4. Chan W
    5. Simonsick EM
    6. Ferrucci L

    (2013) Personality, metabolic rate and aerobic capacity

    PLOS ONE 8:e54746.

    https://doi.org/10.1371/journal.pone.0054746

    • PubMed
    • Google Scholar
73. 1. Terracciano A
    2. Sutin AR

    (2019) Personality and Alzheimer’s disease: an integrative review

    Personality Disorders 10:4–12.

    https://doi.org/10.1037/per0000268

    • PubMed
    • Google Scholar
74. 1. Turiano NA
    2. Chapman BP
    3. Gruenewald TL
    4. Mroczek DK

    (2015) Personality and the leading behavioral contributors of mortality

    Health Psychology 34:51–60.

    https://doi.org/10.1037/hea0000038

    • PubMed
    • Google Scholar
75. 1. Tyrka AR
    2. Parade SH
    3. Price LH
    4. Kao H-T
    5. Porton B
    6. Philip NS
    7. Welch ES
    8. Carpenter LL

    (2016) Alterations of mitochondrial DNA copy number and telomere length with early adversity and psychopathology

    Biological Psychiatry 79:78–86.

    https://doi.org/10.1016/j.biopsych.2014.12.025

    • PubMed
    • Google Scholar
76. 1. Urata M
    2. Koga-Wada Y
    3. Kayamori Y
    4. Kang D

    (2008) Platelet contamination causes large variation as well as overestimation of mitochondrial DNA content of peripheral blood mononuclear cells

    Annals of Clinical Biochemistry 45:513–514.

    https://doi.org/10.1258/acb.2008.008008

    • PubMed
    • Google Scholar
77. 1. Verhoeven JE
    2. Révész D
    3. Picard M
    4. Epel EE
    5. Wolkowitz OM
    6. Matthews KA
    7. Penninx BWJH
    8. Puterman E

    (2018) Depression, telomeres and mitochondrial DNA: between- and within-person associations from a 10-year longitudinal study

    Molecular Psychiatry 23:850–857.

    https://doi.org/10.1038/mp.2017.48

    • PubMed
    • Google Scholar
78. 1. Vyas CM
    2. Ogata S
    3. Reynolds CF
    4. Mischoulon D
    5. Chang G
    6. Cook NR
    7. Manson JE
    8. Crous-Bou M
    9. De Vivo I
    10. Okereke OI

    (2020) Lifestyle and behavioral factors and mitochondrial DNA copy number in a diverse cohort of mid-life and older adults

    PLOS ONE 15:e0237235.

    https://doi.org/10.1371/journal.pone.0237235

    • PubMed
    • Google Scholar
79. 1. Wei YH
    2. Lee CF
    3. Lee HC
    4. Ma YS
    5. Wang CW
    6. Lu CY
    7. Pang CY

    (2001) Increases of mitochondrial mass and mitochondrial genome in association with enhanced oxidative stress in human cells harboring 4,977 BP-deleted mitochondrial DNA

    Annals of the New York Academy of Sciences 928:97–112.

    https://doi.org/10.1111/j.1749-6632.2001.tb05640.x

    • PubMed
    • Google Scholar
80. 1. Xing J
    2. Chen M
    3. Wood CG
    4. Lin J
    5. Spitz MR
    6. Ma J
    7. Amos CI
    8. Shields PG
    9. Benowitz NL
    10. Gu J
    11. de Andrade M
    12. Swan GE
    13. Wu X

    (2008) Mitochondrial DNA content: its genetic heritability and association with renal cell carcinoma

    Journal of the National Cancer Institute 100:1104–1112.

    https://doi.org/10.1093/jnci/djn213

    • PubMed
    • Google Scholar
81. 1. Xu FX
    2. Zhou X
    3. Shen F
    4. Pang R
    5. Liu SM

    (2012) Decreased peripheral blood mitochondrial DNA content is related to hba1c, fasting plasma glucose level and age of onset in type 2 diabetes mellitus

    Diabetic Medicine 29:e47–e54.

    https://doi.org/10.1111/j.1464-5491.2011.03565.x

    • PubMed
    • Google Scholar
82. 1. Yang SY
    2. Castellani CA
    3. Longchamps RJ
    4. Pillalamarri VK
    5. O’Rourke B
    6. Guallar E
    7. Arking DE

    (2021) Blood-Derived mitochondrial DNA copy number is associated with gene expression across multiple tissues and is predictive for incident neurodegenerative disease

    Genome Research 31:349–358.

    https://doi.org/10.1101/gr.269381.120

    • PubMed
    • Google Scholar
83. 1. Zampino M
    2. Brennan NA
    3. Kuo P-L
    4. Spencer RG
    5. Fishbein KW
    6. Simonsick EM
    7. Ferrucci L

    (2020) Poor mitochondrial health and systemic inflammation? test of a classic hypothesis in the Baltimore longitudinal study of aging

    GeroScience 42:1175–1182.

    https://doi.org/10.1007/s11357-020-00208-x

    • PubMed
    • Google Scholar
84. 1. Zhang Q
    2. Raoof M
    3. Chen Y
    4. Sumi Y
    5. Sursal T
    6. Junger W
    7. Brohi K
    8. Itagaki K
    9. Hauser CJ

    (2010) Circulating mitochondrial DAMPs cause inflammatory responses to injury

    Nature 464:104–107.

    https://doi.org/10.1038/nature08780

    • PubMed
    • Google Scholar

Author details

1. Richard F Oppong

   Translational Gerontology Branch, National Institute on Aging, Baltimore, United States

   Contribution

   Conceptualization, Formal analysis, Validation, Investigation, Methodology, Writing – original draft

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-3244-6694

2. Antonio Terracciano

   1. Department of Geriatrics, Florida State University, Tallahassee, United States
   2. Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, United States

   Contribution

   Investigation, Methodology, Writing – original draft

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-5799-8885

3. Martin Picard

   Division of Behavioral Medicine, Department of Psychiatry; Merritt Center and Columbia Translational Neuroscience initiative, Department of Neurology, Columbia University Irving Medical Center; New York State Psychiatric Institute, New York, United States

   Contribution

   Investigation, Methodology, Writing – original draft

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-2835-0478

4. Yong Qian

   Translational Gerontology Branch, National Institute on Aging, Baltimore, United States

   Contribution

   Software, Writing – review and editing

   Competing interests

   No competing interests declared

5. Thomas J Butler

   Translational Gerontology Branch, National Institute on Aging, Baltimore, United States

   Contribution

   Software, Writing – review and editing

   Competing interests

   No competing interests declared

6. Toshiko Tanaka

   Translational Gerontology Branch, National Institute on Aging, Baltimore, United States

   Contribution

   Data curation, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-4161-3829

7. Ann Zenobia Moore

   Translational Gerontology Branch, National Institute on Aging, Baltimore, United States

   Contribution

   Data curation, Writing – review and editing

   Competing interests

   No competing interests declared

8. Eleanor M Simonsick

   Translational Gerontology Branch, National Institute on Aging, Baltimore, United States

   Contribution

   Data curation, Writing – review and editing

   Competing interests

   No competing interests declared

9. Krista Opsahl-Ong

   Translational Gerontology Branch, National Institute on Aging, Baltimore, United States

   Contribution

   Formal analysis, Writing – review and editing

   Competing interests

   No competing interests declared

10. Christopher Coletta

    Laboratory of Genetics and Genomics, National Institute on Aging, Baltimore, United States

    Contribution

    Data curation, Writing – review and editing

    Competing interests

    No competing interests declared

11. Angelina R Sutin

    Department of Behavioral Sciences and Social Medicine, College of Medicine, Florida State University, Tallahassee, United States

    Contribution

    Data curation, Writing – review and editing

    Competing interests

    No competing interests declared

12. Myriam Gorospe

    Laboratory of Genetics and Genomics, National Institute on Aging, Baltimore, United States

    Contribution

    Data curation, Funding acquisition, Investigation, Writing – review and editing

    Competing interests

    No competing interests declared

13. Susan M Resnick

    Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, United States

    Contribution

    Data curation, Investigation, Writing – review and editing

    Competing interests

    No competing interests declared

14. Francesco Cucca

    Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche, Monserrato, Italy

    Contribution

    Data curation, Investigation, Writing – review and editing

    Competing interests

    No competing interests declared

15. Sonja W Scholz

    1. Neurodegenerative Diseases Research Unit, National Institute of Neurological Disorders and Stroke, Bethesda, United States
    2. Department of Neurology, Johns Hopkins University Medical Center, Baltimore, United States

    Contribution

    Data curation, Investigation, Writing – review and editing

    Competing interests

    participates as a Scientific advisory board member for Lewy body dementia association. The author has no other competing interests to declare

16. Bryan J Traynor

    1. Department of Neurology, Johns Hopkins University Medical Center, Baltimore, United States
    2. Laboratory of Neurogenetics, National Institute on Aging, Bethesda, United States

    Contribution

    Data curation, Investigation, Writing – review and editing

    Competing interests

    has indicated several grants, patents, positions and interests in his individual ICMJE form, but he has done it in the interest of transparency and none of his disclosures is related to the content of the current manuscript

17. David Schlessinger

    Laboratory of Genetics and Genomics, National Institute on Aging, Baltimore, United States

    Contribution

    Conceptualization, Data curation, Funding acquisition, Investigation, Writing – original draft

    Competing interests

    is a consultant for Elixirgen Therapeutics, Inc The author has no other competing interests to declare

18. Luigi Ferrucci

    Translational Gerontology Branch, National Institute on Aging, Baltimore, United States

    Contribution

    Conceptualization, Data curation, Formal analysis, Supervision, Funding acquisition, Investigation, Methodology, Writing – original draft

    For correspondence

    ferruccilu@grc.nia.nih.gov

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-6273-1613

19. Jun Ding

    Translational Gerontology Branch, National Institute on Aging, Baltimore, United States

    Contribution

    Conceptualization, Formal analysis, Supervision, Validation, Methodology, Writing – original draft

    For correspondence

    jun.ding@nih.gov

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-4302-5027

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