Background: Excess weight in adulthood leads to health complications such as diabetes, hypertension, or dyslipidemia. Recently, excess weight has also been related to brain atrophy and cognitive decline. Reports show that obesity is linked with Alzheimer’s disease (AD)-related changes, such as cerebrovascular damage or amyloid-β accumulation. However, to date no research has conducted a direct comparison between brain atrophy patterns in AD and obesity. Objective: Here, we compared patterns of brain atrophy and amyloid-β/tau protein accumulation in obesity and AD using a sample of over 1,300 individuals from four groups: AD patients, healthy controls, obese otherwise healthy individuals, and lean individuals. Methods: We age- and sex-matched all groups to the AD-patients group and created cortical thickness maps of AD and obesity. This was done by comparing AD patients with healthy controls, and obese individuals with lean individuals. We then compared the AD and obesity maps using correlation analyses and permutation-based tests that account for spatial autocorrelation. Similarly, we compared obesity and AD brain maps with amyloid-β and tau protein maps from other studies. Results: Obesity maps were highly correlated with AD maps but were not correlated with amyloid-β/tau protein maps. This effect was not accounted for by the presence of obesity in the AD group. Conclusion: Our research confirms that obesity-related grey matter atrophy resembles that of AD. Excess weight management could lead to improved health outcomes, slow down cognitive decline in aging, and lower the risk for AD.

Identifying early signs of neurodegeneration due to Alzheimer's disease (AD) is a necessary first step towards preventing cognitive decline. Individual cortical thickness measures, available after processing anatomical magnetic resonance imaging (MRI), are sensitive markers of neurodegeneration. However, normal aging cortical decline and high inter-individual variability complicate the comparison and statistical determination of the impact of AD-related neurodegeneration on trajectories. In this paper, we computed trajectories of thickness changes in a 2D representation of a 62-dimensional manifold of individual cortical thickness measures. To compute this representation, we used a novel, nonlinear dimension reduction algorithm called Uniform Manifold Approximation and Projection (UMAP). We first trained a UMAP embedding on cortical thickness measurements of 6,237 cognitively healthy participants (3,556 women) aged 18 to 100 years old. Once transformed and rotated, its principal axis was shown to be positively associated (r= 0.65) with participants' age. We then projected in this transformed UMAP space, data from longitudinal MRIs of 537 mild cognitively impaired (MCI) subjects and 340 AD subjects from the Alzheimer's Disease Neuroimaging Initiative database. Each participant had multiple visits (n≥ 2), one year apart. Once in the transformed UMAP space, the data was clustered using ak-means variant. Average trajectories between clusters were shown to be significantly different between MCI and AD subjects. Moreover, we showed that some clusters and trajectories between clusters were more prone to host AD subjects. This study was able to differentiate AD and MCI subjects based on their cluster trajectory in a 2D space with a AUC of 0.72 over 2,000 iterations.