Elsevier

Neurobiology of Aging

Volume 67, July 2018, Pages 1-9
Neurobiology of Aging

Regular article
Negative fateful life events in midlife and advanced predicted brain aging

https://doi.org/10.1016/j.neurobiolaging.2018.03.004Get rights and content

Abstract

Negative fateful life events (FLEs) such as interpersonal conflict, death in the family, financial hardship, and serious medical emergencies can act as allostatic stressors that accelerate biological aging. However, the relationship between FLEs and neuroanatomical aging is not well understood. We examined 359 men (mean age 62 years) participating in the Vietnam Era twin study of aging (VETSA) to determine whether negative midlife FLEs are associated with advanced brain aging after controlling for physical, psychological, and lifestyle factors. At two different time points, participants were assessed for negative FLEs, health and well-being, general cognitive ability, socioeconomic status, depression, and ethnicity. Participants underwent a magnetic resonance imaging examination, and T1-weighted images were processed with FreeSurfer. Subsequent neuroanatomical measurements were entered into the Brain-Age Regression Analysis and Computation Utility software (BARACUS) to predict brain age. Having more midlife FLEs, particularly relating to interpersonal relationships, was associated with advanced predicted brain aging (i.e., higher predicted brain age relative to chronological age). This association remained after controlling for the significant covariates of alcohol consumption, cardiovascular risk, adult socioeconomic status, and ethnicity.

Introduction

It has been hypothesized that chronic exposure to prolonged stressful situations can result in biological weathering and premature aging (Geronimus, 2013, Geronimus et al., 2010). Stress has been shown to exert a disruptive effect on biological systems resulting in oxidative stress, mitochondrial damage, immunosenescence, endocrinosenescence, as well as epigenetic modifications of the sympathetic nervous system, hypothalamic-pituitary-adrenal axis, immune system, and other metabolic processes (Cole, 2014, Jenny, 2012, Jenny et al., 2012). Adverse circumstances such as economic hardship, low education, and community disadvantage have been associated with various chronic, age-related diseases such as type 2 diabetes, coronary heart disease, stroke, and dementia (Fraga et al., 2015, Gruenewald et al., 2009, Hemingway et al., 2003, Koster et al., 2006, Loucks et al., 2007, Loucks et al., 2009, Loucks et al., 2010, Robertson et al., 2015, Smith et al., 2011). Cumulative lifetime stress, but not childhood or recent stress, has been shown to accelerate epigenetic (DNA methylation based) aging in an urban, African-American cohort (Zannas et al., 2015). Furthermore, a study of African-American middle-aged women of low socioeconomic status (SES) showed advanced biological aging as determined by leukocyte telomere length (Simons et al., 2016). Interestingly, this effect was predominately influenced by everyday financial pressure over other factors such as diet, exercise, smoking, alcohol consumption, and having health insurance, suggesting that certain types of psychological distress can significantly accelerate biological aging. What remains to be determined is if exposure to these negative fateful life events (FLEs) in midlife is related to brain aging, and if this effect is associated with SES and/or ethnicity.

Many studies have characterized normal brain aging and pathologically advanced aging in dementias. The most common metric of normal brain aging is gross brain volume reductions of 0.2%–0.5% per year (Enzinger et al., 2005; Ezekiel et al., 2004; Fotenos et al., 2005; Hedman et al., 2012; Scahill et al., 2003). Cortical volume reductions of around 0.5% per year are found across the brain surface in most regions (Fjell et al., 2014). There is, however, considerable interindividual variability in the magnitude of brain changes with age; the degree of brain aging can be adversely affected by poor physical and mental health. Cardiovascular risk factors such as hypertension, diabetes, and obesity have been shown to be associated with advanced brain aging (Leritz et al., 2011, Ronan et al., 2016). Regarding mental health, FLEs and stress are highly associated with depression, and depression has been associated with reductions in hippocampal volume (Schmaal et al., 2016) and cortical thickness within the orbitofrontal cortex, cingulate, insula, and temporal lobes (Schmaal et al., 2017). Furthermore, a retrospective study of 1271 older adults found that 78% of dementia patients had a stressful life event before the onset of dementia compared to 55% for control subjects (Tsolaki et al., 2010). How FLEs affect people in late middle age with and without mild cognitive impairment (MCI) or at high genetic risk of dementia (i.e., apolipoprotein E4 [APOE-ε4] allele carrier) is yet to be assessed. Therefore, examining the association between FLEs and advanced brain aging needs to take into account physical and mental health factors that are thought to adversely affect neuroanatomy.

With computational advancement, quantifying healthy brain aging has evolved from retrospective correlative analysis to several predictive models of brain aging. One model (Liem et al., 2017) combines measures of cortical thickness, cortical surface area, and subcortical volumes to calculate a predicted brain age. The mean absolute prediction error of this model is 4.29 years, which is similar to other models of predicted brain age (Cole et al., 2015, Cole et al., 2017, Franke et al., 2010). More importantly, the difference between predicted brain age and chronological age (predicted brain age difference [PBAD]) was shown to be sensitive to cognitive impairment; that is, higher PBAD was associated with worse objective cognitive impairment (Liem et al., 2017). Predictions were resilient to head motion artifacts and generalizable to other data sets, although differences in scanner, sequence, and head coil may still influence estimates of predicted brain age. This model has been made publicly available in an easy-to-use application called Brain-Age Regression Analysis and Computation Utility Software (BARACUS).

The aims of this project were to investigate the extent to which negative FLEs are associated with advanced predicted brain aging in a group of late-middle-aged men as assessed by magnetic resonance imaging and PBAD using BARACUS (Liem et al., 2017). Considering the findings that lifetime stress accelerates epigenetic aging (Zannas et al., 2015) and telomere length reduction (Simons et al., 2016), we hypothesized that higher total FLE scores would be associated with a brain age greater than chronological age. We also sought to identify whether FLEs concerning relationships, finances, and/or health would be associated with advanced PBAD. We hypothesized that financial stress would be a significant factor based on previous findings in middle-aged African-American women (Simons et al., 2016). Finally, we tested the relationship between FLEs and PBAD after controlling for covariates. We hypothesized that the relationship between FLEs and PBAD would be influenced by physical health complications, ethnicity, and/or SES. We also sought to delineate the effect of FLEs on brain aging from potential confounding factors, namely neuroanatomical changes due to traumatic brain injury (TBI), MCI, or genetic risk for Alzheimer's disease based on APOE-ε4 status.

Section snippets

Participants

Participants in the Vietnam Era twin study of aging (VETSA) magnetic resonance imaging (MRI) cohort (Kremen et al., 2010) were recruited from the Vietnam Era twin registry, a nationally distributed sample of male-male twin pairs who served in the United States military at some point between 1965 and 1975 (Goldberg et al., 2002, Tsai et al., 2013). Participants have similar health and lifestyle characteristics to American men in their age range (Schoenborn and Heyman, 2009). Although all VETSA

Fateful life events and covariates

The prevalence of FLEs between the first (mean total FLEs = 1.62, SD 1.77) and second time points (mean total FLEs = 1.56, SD 1.62) was slightly but significantly reduced [β = 0.09, standard error (SE) 0.021, z (354) = 4.10, p < 0.001]. Combined, the mean total FLEs was 3.06 (SD 2.17; Table 1). When comparing low (0–2), moderate (3–5) to high (≥6) FLE groups, the prevalence of FLEs in the domains of relationships, finances, and health significantly increased (Table 1). There was a significant

Discussion

We showed here that FLEs in midlife are associated with advanced predicted brain aging, although the nature of that relationship is complex. When all significant covariates are taken into account, on average one FLE is associated with an increase in PBAD by 0.37 years. Those with more alcohol consumption, greater cardiovascular risk, lower adult SES, and whose race/ethnicity was other than non-Hispanic white had older brain age than chronological age. However, not all factors were associated

Conclusion

In middle age, cardiovascular risk factors, low adult SES, alcohol consumption, and ethnicity were significantly associated with advanced predicted brain age. Even after controlling for these factors, individuals who had higher levels of major life events showed signs of advanced predicted brain aging. Although post-hoc analysis showed that nonamnestic, but not amnestic, MCI was also associated with advanced predicted brain aging, this association did not hold up after controlling for these

Disclosure statement

AMD is a founder and holds equity in CorTechs Laboratories, Inc., and also serves on its Scientific Advisory Board. The terms of this arrangement have been reviewed and approved by the University of California, San Diego, in accordance with its conflict of interest policies. All other authors report no potential conflicts of interest.

Acknowledgements

The content of this article is the responsibility of the authors and does not represent official views of NIA/NIH or the Veterans' Administration. Numerous organizations provided invaluable assistance in the conduct of the VET Registry, including: U.S. Department of Veterans Affairs, Department of Defense; National Personnel Records Center, National Archives and Records Administration; Internal Revenue Service; National Opinion Research Center; National Research Council, National Academy of

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