Investigating Perceptions of the Eating Disorder Examination-Questionnaire Among Undergraduate Students: A Qualitative Approach.

Alzheimer’s disease (AD) is the leading cause of cognitive decline and functional disability among people over the age of 60 years. AD has limited treatment options, and therefore efforts to prevent or delay its onset are the priority for ensuring the health of aging population [, ]. The gradual, cumulative brain changes that lead to AD occur over several decades prior to the first signs of cognitive impairment and this provides a window of opportunity for disease-modifying/prevention interventions [–]. Yet the life-long action of AD risks on an individual is not well-understood and this limits the efforts to evaluate personalized risk mitigation strategies that may delay or prevent AD []. Existing approaches are focused on early diagnosis of people at risk for AD and use a combination of amyloid- or tau-sensitive positron emission tomography (PET), blood and cerebrospinal fluid (CSF) analysis, MRI-based brain morphometry, and neurobehavioral clinical/cognitive assessments []. These approaches straddle sensitivity versus specificity versus cost, invasiveness, and availability concerns and may not be suitable for capturing both effects of AD risk factors in typically aging samples and for predicting conversion to AD in high-risk individuals. Structural MRI comes close to this because it is a non-invasive and widely available alternative for assessing preclinical AD risk. However, clinical MRI-based findings in AD, such as hippocampal shrinkage and ventricular enlargement, are non-specific and typically emerge after the onset of prodromal symptoms and thus cannot be used as early warning biomarker [–]. To address these limitations, we propose an alternative strategy that uses structural MRI to capture regional similarities to AD-specific brain changes (i.e., brain deficit patterns) [–]. We first conducted a meta-analysis of regional MRI effect sizes comparing amyloid PET-confirmed AD cases and amyloid-negative controls. From this, we ranked AD’s impact on brain structures and used this regional pattern to develop a Regional Vulnerability Index (RVI) for AD. RVI-AD is measuring the agreement between an individuals’ brain and the characteristic brain patterns in AD, rather than focusing solely on measuring individual brain structures such as the hippocampus or temporal cortex. Other multivariate approaches combine biomarkers, including amyloid pattern similarity scores [], into a unifying measure using complex machine and deep learning methods [–]. In contrast, RVI-AD is a linear measure applied to standard anatomical brain MRI. Here, we used RVI-AD to test the hypotheses that [] known risk factors for AD act throughout adulthood, leading to the gradual formation of AD brain deficit patterns, and [] the development of these patterns may predict dementia onset. If RVI-AD can capture these processes, it may allow for a mechanistic evaluation of how risk factors influence the brain’s structural progression from healthy aging to AD over time. The first hypothesis was tested by tracking the actions of two established risk factors for AD in healthy adults. The apolipoprotein E (APOE) ε4 allele is the best-validated genetic risk factor for the late-onset form of AD, associated with a 3- to 15-fold elevation of the risk of developing AD [–] in people of central European ancestry []. APOE is a cholesterol- and triglyceride-transporting lipoprotein with lipid binding sites [] and has three major isoforms in humans []. Both homozygous and heterozygous carriers of the ε4 isoform have a higher affinity for transporting low-density cholesterol and face a higher risk of developing AD with 30–60% developing dementia by age 85 []. We hypothesized that RVI-AD may capture brain abnormalities linked to AD [–] in the brains of healthy ε4 allele carriers.