Appraising the Role of Iron in Brain Aging and Cognition: Promises and Limitations of MRI Methods

Abstract

Age-related increase in frailty is accompanied by a fundamental shift in cellular iron homeostasis. By promoting oxidative stress, the intracellular accumulation of non-heme iron outside of binding complexes contributes to chronic inflammation and interferes with normal brain metabolism. In the absence of direct non-invasive biomarkers of brain oxidative stress, iron accumulation estimated in vivo may serve as its proxy indicator. Hence, developing reliable in vivo measurements of brain iron content via magnetic resonance imaging (MRI) is of significant interest in human neuroscience. To date, by estimating brain iron content through various MRI methods, significant age differences and age-related increases in iron content of the basal ganglia have been revealed across multiple samples. Less consistent are the findings that pertain to the relationship between elevated brain iron content and systemic indices of vascular and metabolic dysfunction. Only a handful of cross-sectional investigations have linked high iron content in various brain regions and poor performance on assorted cognitive tests. The even fewer longitudinal studies indicate that iron accumulation may precede shrinkage of the basal ganglia and thus predict poor maintenance of cognitive functions. This rapidly developing field will benefit from introduction of higher-field MRI scanners, improvement in iron-sensitive and -specific acquisition sequences and post-processing analytic and computational methods, as well as accumulation of data from long-term longitudinal investigations. This review describes the potential advantages and promises of MRI-based assessment of brain iron, summarizes recent findings and highlights the limitations of the current methodology.

Accumulation of iron in the putamen predicts its shrinkage in healthy older adults: A multi-occasion longitudinal study

“…The cumulative record thus far supports the proposition that brain iron accumulation may be a meaningful biomarker of impending structural and cognitive declines in aging and disease (Schenck and Zimmerman 2004; Walsh et al 2013; Ward et al 2014; Daugherty and Raz 2015). Nonetheless, the temporal relationship between iron accumulation and structural changes in the brain is unclear.…”

“…Although the sample was, on average, normotensive, in the course of study, as many as 11 participants had clinically diagnosed or observed hypertension (blood pressure measurement at least 140 mm Hg systolic or 90 mmHg diastolic; see Table 1). Hypertension was determined categorically at each assessment and composited into a frequency score—ranging 0 (normotensive at all assessments) to 1 (hypertensive at all assessments)—that was tested as a cardiovascular risk factor to potentially explain age-related variability in iron accumulation and shrinkage (see Daugherty et al 2015; Daugherty and Raz 2015). …”

“…Since recent advances in magnetic resonance imaging (MRI) methods for iron estimation, studies of lifetime differences in brain iron content have proliferated (see Haacke et al 2005; Daugherty and Raz 2013, 2015 for reviews). The cumulative record thus far supports the proposition that brain iron accumulation may be a meaningful biomarker of impending structural and cognitive declines in aging and disease (Schenck and Zimmerman 2004; Walsh et al 2013; Ward et al 2014; Daugherty and Raz 2015).…”

“…Moreover, because all extant longitudinal in vivo studies of brain iron involved only two measurement occasions, the temporal order of regional iron accumulation and loss of volume in the brain could not be assessed (see Daugherty and Raz 2015 for a review). The variance partitioning approach in cross-sectional mediation analyses (e.g., Rodrigue et al 2013) cannot reveal the temporal order of events and the relationship between them (Lindenberger et al 2011).…”

“…Iron content was estimated via R2* relaxometry in the neostriatum, a technique that has demonstrated reliability and validity (Daugherty and Raz 2015; Daugherty et al 2015). These measures of iron content were combined with regional volumes in models evaluated with a latent-variable longitudinal modeling technique—simple and parallel change latent growth curve analyses.…”