Identifying lifestyle factors that promote brain resilience in ApoE4 carriers. (20th December 2022)

• Record Type: Journal Article
• Authors: Haddad, Elizabeth; Javid, Shayan; Zhu, Alyssa H; Nir, Talia M; Gadewar, Shruti; Gari, Iyad Ba; Lam, Pradeep; Gupta, Arpana; Thompson, Paul M; Jahanshad, Neda
• Is Part Of: Alzheimer's & dementia. Volume 18(2022)Supplement 6
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-12-20
• Subjects: Alzheimer's disease -- Periodicals; Alzheimer Disease -- Periodicals; Dementia -- Periodicals; Démence; Maladie d'Alzheimer; Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 616.83
• DOI: 10.1002/alz.068013
• Languages: English
• ISSNs: 1552-5260

Abstract

Abstract: Background: Apolipoprotein (ApoE) E4 (e4) carriers are at increased risk for Alzheimer's disease (AD) (Strittmatter, 2012), which is often characterized by accelerated hippocampal atrophy and cognitive decline after 65 (Reinvang, 2013). However, not all e4 carriers develop AD. In those at heightened genetic risk, it is important to evaluate lifestyle factors that might slow brain aging, and delay dementia onset. Poorer sleep quality, physical activity, and diet have been associated with several AD‐related processes ‐ including increased beta‐amyloid burden, decline in vascular integrity, reduced glucose metabolism, and neuronal dysfunction (Mander 2017, Radak 2010, Sezgin 2014). Smoking (Durazzo, 2014) and excessive alcohol consumption (Venkataraman, 2017) have also been shown to promote AD pathology and risk. Moreover, social contact and education may enhance cognitive reserve (Livingston, 2020). Here, we investigated the role of these factors in promoting structural brain resilience in a population susceptible to AD from the UK Biobank (UKB; Miller, 2016). Method: UKB participants over age 65 with e3e4 or e4e4 genotype were included (1415M, 1355F). A novel deep learning method (Lam, 2020) was used to calculate predicted brain age (a machine‐learning estimate of a person's age from their brain MRI). Participants were labeled as resilient if their predicted age was 3+ years younger than their actual age. Lifestyle factors evaluated may be found in Table 1 . Association rule learning (Agrawal, 1993) using the mlxtend python library was used to identify sets of factors most often associated with resiliency. Result: 604/2770 of the e4 carriers were labeled as resilient (Figure 1 ). The antecedent set with the highest likelihood (42%) of being associated with resiliency included following the ideal recommendations for physical activity, infrequent alcohol consumption, averaging at least 7 hours of sleep/night, participating in leisure/social activities, and never having smoked. These measures also appeared in other likely antecedent sets (Table 2 ). Conclusion: We identified several lifestyle factors that may promote resilience to brain aging in those at elevated risk for AD. We aim to replicate our findings using a larger dataset with brain age calculated, and train sex‐specific models as well as testing them in people of more diverse ancestry.