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).

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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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