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S O U R C E : AgING
In countries with advanced economies, changes in age distributions, largely due to lower birth rates and increased life expectancy, has meant that the world’s population is increasingly older, with the number of persons over 80 projected to triple by 2050 [1]. Paradoxically, the success and opportunities presented by this new longevity has often been over-shadowed by the many challenges that come with a “top heavy” society. Specifically, the growing number of older adults have created an unprecedented demand on health care services, with increased vulnerability to cognitive decline, appreciable loss of autonomy, and need for institutional care threatening the economic security of families, communities and countries. With an estimated 47 million people currently diagnosed with dementia worldwide [2,3], the necessity of responding to these challenges are vital. In view of this, what was once referred to as ‘the elephant in the room’ [4] – i.e., the role of aging in cognitive decline – has now transpired into a scientifically challenging and compelling pursuit, the search to identify the constituents of a healthy brain and mind across the human lifespan [5–11].
Given the complexity of the aging process, at present there is no gold standard selection of age-related markers for assessing cognitive decline and disease progression in older adults [6–9,11–18]. Furthermore, with considerable individual variability observed in aging trajectories, identifying the various factors that may underlie this individualism has not been a trivial task [19]. Prior studies investigating lifespan influences on non-pathological aging have identified childhood IQ, socioeconomic position (SEP), and genetic markers as some of the most consistent predictors of later-life health outcome [6,8,12,14,20–31]. In particular, of these, childhood cognitive ability has been identified as the strongest determinant of later-life intelligence explaining ~50% of the variance in cognition even at age ~80 [9,25–27]. Consequently, a series of other possible determinants (physical activity, tobacco smoking, hypertension, obesity, reduced cardiac output, nutrition), with smaller, but significant effects on cognitive function and brain aging have been identified that may explain the remaining variability [6–11,17,18,22,32–35]. However, many of these measures have been branded as proxy markers of lower early-life intelligence.
For both diagnostic and research purposes, multimodal magnetic resonance imaging (MRI) is a popular choice for exploring age-related brain correlates of cognitive change [36–48]. To date, the resulting body of evidence converges on age-related decreases cross-sectionally and longitudinally, in the density of dopaminergic receptors [49], cortical thickness [44], whole-brain and regional volumes [46,50–52], and increases in ventricular volume [53], and the emergence of neural insults of cerebrovascular origin [33,36,54,55]. Furthermore, much of the age-related variation in brain structure has shown regional and temporal specificity, with frontal, parietal, and temporal lobes appearing most vulnerable to age-effects and the occipital lobe the least [8,10,56–62]. These findings are consistent with the anterior-to-posterior gradient of age-related brain deterioration – first coined in 1881 by French Philosopher Theodule Ribot when he introduced the concept of “Loi de regression” (i.e., last in, first out) – to describe memory formation and destruction [63]. Specifically, “first out” brain regions are characterized by a more complex architecture, a protracted ontogenetic developmental course, and are more likely to provide support when faced with neural insults, maladaptive brain function, or higher-order cognitive tasks [64–66].
Despite significant individual differences in aging trajectories, the overall consensus on age-related effects on brain health and cognition ability is clear: the brain shrinks with advancing age with alterations observed at both the molecular and morphological level, and these changes are linked to declines in specific cognitive domains [47,67,68]. Of these, processing speed, executive functioning, working memory, and inhibitory functions are the cognitive domains reported as most vulnerable to advancing age, whilst implicit procedural long-term memory, numerical processing, and the general knowledge accrued across the lifespan are those that appear relatively spared [6,7,9,10,37,47,68–70].
Currently, much of our knowledge on brain and behavior changes are derived from cross-sectional studies that compare single observations from individuals of different ages – most commonly groups of young and extremely old adults. Although suitable for identifying population-level mean trends, and efficient in terms of time and cost, cross-sectional studies are vulnerable to cohort effects, selection bias, and by design, can only offer insight on age-related-differences [37,43,50,52,62,65,71–73]. As aging research is essentially the study of change, a preferred approach of extracting individual differences in change – independently of individual differences in level – whilst simultaneously permitting the study of developmental and maturational trends is to use a longitudinal design with multiple follow-up assessments. Thus, to expand on prior efforts, we use within-subject (longitudinal) behavioral measurements that span across critical periods of the human lifespan using members of a prospective study; the 1953 Metropolitan Danish Male Birth Cohort (MDBC-1953) [14,74]. Specifically, following a major revitalization in 2009, research efforts based on MDBC-1953 data has focused on age-related cognitive decline. Here, the main aim is to elucidate why some older adult’s cognitive abilities are preserved well into late adulthood, while others demonstrate rapid decline.
In order to optimize the possibility of cognitive ability in a late-midlife being found to be associated with biological (or other) correlates, we exploited the long-term nature of this study to identify individuals with a relatively large decline in cognitive ability from early-adulthood (“decliners”), and those who show improvement (“improvers”). This standard approach, formally known as the Extreme Groups Design (EGD) [75], also maximizes the subject variability in other relevant factors such as education attainment, occupational complexity and levels of motivation, increasing the generalizability of this study to the real population. That is, in what is otherwise a highly homogenous cohort, the EGD attenuates the commonly observed selection bias of self-selected healthy study samples towards high-functioning and educated individuals. Further benefits of using this cohort are manifold. First, there is a lack of evidence suggesting that pathological change abruptly begins at old age after a period of relative stability. Thus, inclusion of childhood, youth and late midlife cognitive scores in aging studies may be key to predicting later-life health outcomes [8,13,67,68]. Second, a homogenous, late-midlife cohort provides conditions that are optimal for assessing the influence of potential candidate determinants on late-life morbidity without confounding cohort or other age factors. Third, findings from the extant literature exploring brain markers of developmental and aging processes have described age-associated changes as “early development in reverse” [42,64,66]. Thus, phenotyping across the human lifespan and not just the extremes of the age-range is optimal when exploring normative or pathological brain aging patterns [9,15,37,68,69,76]. Specifically, if the brain’s ‘blueprint for aging’ has already developed by preschool years, the conservative approach of selecting the oldest of old to expose biomarkers of normative aging is an outdated one [25–27]. Lastly, even when data are derived from a longitudinal study, many factors (demographic, lifestyle etc.) that may contribute to the observed heterogeneity in aging trajectories are ignored which ultimately undermines the reliability of the relationships discovered.
Considering this, our primary focus was to explore the factors—general health, vascular, demographic and lifestyle—that contribute to normative brain and cognitive aging. Crucially, we are not interested in just the age-dependence in any individual physiological or behavioral component, but a holistic range of endogenous and exogenous influences accrued across the lifespan. To achieve this, we distinguish between life course influences that act to preserve health (“positive influences”) versus those that are implicated in its demise (“negative influences”). This approach has the potential to identify specific brain and cognitive patterns that may underlie differential aging trajectories, whilst simultaneously exposing the relation of these patterns to a broad range of modifiable risk and protective factors. By modelling multiple variables of multiple modalities simultaneously we provide a more precise estimate of their synergetic effects filling a gap in the existing aging literature. Furthermore, our inclusion of both bivariate and multi-level analyses allows for both specific and general relations to be explored, which are potentially more informative than either approach alone. Specifically, this study goes beyond just investigating the interrelations among a selection of variables; rather, we are seeking specific age-related patterns of brain structure that are associated to sets of correlated cognitive, demographic, health, and behavior variables, as brain-behavior modes of population covariation.
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F O R T H E R E S T O F T H E S T U D Y , P L E A S E V I S I T T H E S O U R C E
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Edited by Engadin, 10 September 2019 - 04:36 PM.