Your brain is only as old as you feel

Kwak S, Kim H, Chey J and Youm Y (2018) Feeling How Old I Am: Subjective Age Is Associated With Estimated Brain Age. Front. Aging Neurosci. 10:168. doi: 10.3389/fnagi.2018.00168

Abstract: While the aging process is a universal phenomenon, people perceive and experience one’s aging considerably differently. Subjective age (SA), referring to how individuals experience themselves as younger or older than their actual age, has been highlighted as an important predictor of late-life health outcomes. However, it is unclear whether and how SA is associated with the neurobiological process of aging. In this study, 68 healthy older adults underwent a SA survey and magnetic resonance imaging (MRI) scans. T1-weighted brain images of open-access datasets were utilized to construct a model for age prediction. We utilized both voxel-based morphometry (VBM) and age-prediction modeling techniques to explore whether the three groups of SA (i.e., feels younger, same, or older than actual age) differed in their regional gray matter (GM) volumes, and predicted brain age. The results showed that elderly individuals who perceived themselves as younger than their real age showed not only larger GM volume in the inferior frontal gyrus and the superior temporal gyrus, but also younger predicted brain age. Our findings suggest that subjective experience of aging is closely related to the process of brain aging and underscores the neurobiological mechanisms of SA as an important marker of late-life neurocognitive health.

Introduction

Subjective age (SA) refers to how individuals experience themselves as younger or older than their chronological age. Subjective perception of aging does not coincide with the chronological age and shows large variability among individuals (Rubin and Berntsen, 2006). The concept of SA has been highlighted in aging research as an important construct because of its relevance to late-life health outcomes. Previous studies have suggested that SA is associated with various outcomes, including physical health (Barrett, 2003; Stephan et al., 2012; Westerhof et al., 2014), self-rated health (Westerhof and Barrett, 2005), life satisfaction (Barak and Rahtz, 1999; Westerhof and Barrett, 2005), depressive symptoms (Keyes and Westerhof, 2012), cognitive decline (Stephan et al., 2014), dementia (Stephan et al., 2016a), hospitalization (Stephan et al., 2016b) and frailty (Stephan et al., 2015a). Although chronological age is a primary factor in explaining these late-life health outcomes, these studies suggest that SA can be another construct that characterizes individual differences in the aging process.

The interoceptive hypothesis posits that a significant number of functions, both physical and cognitive, decline with age and this is subsequently followed by an awareness of such age-related changes (Diehl and Wahl, 2010). In other words, feeling subjectively older may be a sensitive marker or indicator reflecting age-related biological changes. This hypothesis is supported by several studies that have reported significant associations between older SA and poorer biological markers, including C-reactive protein (Stephan et al., 2015c), diabetes (Demakakos et al., 2007), and body mass index (Stephan et al., 2014). Moreover, the indices of biological age (MacDonald et al., 2011) including peak expiratory flow and grip strength, also were associated with SA, even after demographic factors, self-rated health and depressive symptoms were controlled for Stephan et al. (2015b). Among a variety of biological aging markers, a decrease in neural resource constitutes a major dimension of age-related changes in addition to physical, socio-emotional and lifestyle changes (Diehl and Wahl, 2010). Together with the interoceptive hypothesis, the subjective experience of aging may partly result from one’s subjective awareness of age-related cognitive decline. For example, subjective reports of one’s own cognitive decline have received attention as an important source of information for the prediction of subtle neurophysiological changes. Even when no signs of decline are found in cognitive test scores, subjective complaints of cognitive impairment may reflect early stages of dementia or pathological changes in the brain (de Groot et al., 2001; Reid and MacLullich, 2006; Stewart et al., 2008; Yasuno et al., 2015). It is, thus, possible to examine a link between the subjective experience of aging and neurophysiological aging.

To assess age-related brain structural changes and widespread loss of brain tissue, neuroanatomical morphometry methods have been widely used (Good et al., 2001; Fjell et al., 2009a; Raz et al., 2010; Matsuda, 2013). Moreover, the large neuroimaging datasets and newly developed machine learning techniques have made it possible to estimate individualized brain markers (Gabrieli et al., 2015; Cole and Franke, 2017; Woo et al., 2017). This approach can be advantageous for interpreting an individualized index for brain age, since predictive modeling can represent multivariate patterns expressed across whole brain regions, unlike massive and iterative univariate testing. In recent studies, estimated brain age was found to predict indicators of neurobiological aging, including cognitive impairment (Franke et al., 2010; Franke and Gaser, 2012; Löwe et al., 2016; Liem et al., 2017), obesity (Ronan et al., 2016) and diabetes (Franke et al., 2013).

Although SA has predictive values for future cognitive decline or dementia onset, few studies have examined the neurobiological basis of such outcomes. Combining both regional morphometry and the brain age estimation method, this study will provide an integrated picture of how each individual undergoes a heterogeneous brain aging process and supply further evidence of the neural underpinnings of SA (Kotter-Grühn et al., 2015). Using analyses for voxel-based morphometry (VBM) and age-predicting modeling, we aimed to identify whether younger SA is associated with larger regional brain volumes and lower estimated brain age. We also examined possible mediators, including self-rated health, depressive symptoms, cognitive functions and personality traits that were candidates for explaining the hypothesized relationship between SA and brain structures.