Lifestyle – CHETELAT'S LAB



The Lifestyle research group aims at understanding the brain mechanisms underlying the effects of lifestyle factors from normal aging to Alzheimer’s disease dementia.

To this aim we use multimodal neuroimaging – structural MRI, functional MRI, FDG and amyloid-PET – in combination with lifestyle assessments – cognitive, physical, social engagement and diet.

We are especially interested in (i) understanding the brain mechanisms of reserve across disease stages, (ii) assessing the combined/relative effects of lifestyle factors on neuroimaging measurements (iii) identifying critical windows of time where exposure to positive lifestyle factors may have a greater impact and iv) assessing the interaction between lifestyle factors and other nonmodifiable risk factors, including sex and genetics. 


There is growing evidence in the literature that we could modify the course of neurodegenerative diseases, and brain and mental health in general, by modifying our lifestyle. A recent review suggests that ~35% of Alzheimer’s disease (AD) cases could be due to modifiable risk factors, including lifestyle factors (Livingston et al., Lancet, 2017). To further our understanding of the mechanisms by which lifestyle might prevent some AD cases, we investigate the associations between lifestyle and markers of brain integrity. Thus, in a previous set of studies, we assessed the links between lifestyle factors and different neuroimaging measures including markers of AD.

Assessing the relationships between years of education and brain volume, metabolism and connectivity, we showed that, in healthy elderly with no evidence for beta-amyloid [Aβ] deposition, there was a positive association between education and brain volume and metabolism, especially in the anterior cingulate cortex (Arenaza-Urquijo et al., Neuroimage, 2013). Moreover, the connectivity of this region increased with increasing years of education especially with the hippocampus and posterior cingulate cortex, two regions particularly important in AD. By contrast, in a collaborative project conducted on asymptomatic older adults including individuals with Aβ deposition, we found negative relationships between education and brain metabolism and connectivity (Bastin et al., Neuroimage, 2012). We think that these apparently discrepant findings with positive versus negative relationships, also found in the literature, reflect the progression from neuroprotective to compensation processes over the course of the disease, which we summarized in an integrative model (Arenaza-Urquijo et al., Front. Aging Neurosci., 2015).

Adapted from Arenaza-Urquijo et al., Front Aging Neurosci 2015

Thus, in individuals without AD pathology, education is related to increased brain performances while, when AD-related pathology appears, education is related to increased resistance to brain lesions so that, at the same level of cognitive impairment, more lesions will be found in those with higher education. This model was supported by a recent study where we showed that higher education was associated with lower Aβ deposition in cognitively unimpaired older adults but with higher Aβ deposition in mild cognitive impairment (Arenaza-Urquijo et al., Neurobiol Aging., 2017). Moreover, in the same study we found increased FDG-PET uptake with higher education in MCI patients within the regions of higher amyloid-PET uptake, suggesting the existence of a compensatory increase in glucose metabolism. Overall, these findings suggest that early intellectual enrichment may have a differential influence through the course of the disease and may be first protective in healthy asymptomatic elderly, while at the symptomatic stage it is associated with compensation mechanisms to cope with Aβ pathology.

Another relevant aspect to be further investigated in this area is the relative impact of different lifestyle factors. We started to assess this question by investigating the specific relationships between cognitive versus physical activity engagement during late-adulthood and gray matter volume (GM) in cognitively unimpaired older adults. We showed independent relationships of the two lifestyle factors in both common and distinct brain areas, and found that the effects of late life cognitive and physical activity were independent from early cognitive engagement, as reflected by years of education (Arenaz-Urquijo et al., Brain Imaging Behav., 2017).

Finally, we studied the interaction between lifestyle and genetic risk factors of AD. Doing so, we showed that higher education was able to counteract the effects of APOE ε4 on metabolism (independently from Aβ deposition), as increased metabolism with education was found in APOE ε4 carriers in critical regions that sustain episodic memory performance (Arenaza-Urquijo et al., Neurology, 2015). These results suggest that lifestyle may have a differential effect according to individuals’ characteristics, including their genetic predispositions.

Further works are needed to understand the specific and synergic effects of different lifestyle factors, in different lifetime periods and groups (according to their genetics/sex), as this information is crucial to design optimal non-pharmacological (preventive and therapeutic) intervention programs.