Cancer and Alzheimers disease (AD) are two common disorders for which the final pathophysiological mechanism is not yet clearly defined. In a prospective longitudinal study we have previously shown an inverse association between AD and cancer, such that the rate of developing cancer in general with time was significantly slower in participants with AD, while participants with a history of cancer had a slower rate of developing AD. In cancer, cell regulation mechanisms are disrupted with augmentation of cell survival and/or proliferation, whereas conversely, AD is associated with increased neuronal death, either caused by, or concomitant with, beta amyloid (Aβ) and tau deposition. The possibility that perturbations of mechanisms involved in cell survival/death regulation could be involved in both disorders is discussed. Genetic polymorphisms, DNA methylation or other mechanisms that induce changes in activity of molecules with key roles in determining the decision to “repair and live”- or “die” could be involved in the pathogenesis of the two disorders. As examples, the role of p53, Pin1 and the Wnt signaling pathway are discussed as potential candidates that, speculatively, may explain inverse associations between AD and cancer.

Brain clocks, which quantify discrepancies between brain age and chronological age, hold promise for understanding brain health and disease. However, the impact of diversity (including geographical, socioeconomic, sociodemographic, sex and neurodegeneration) on the brain-age gap is unknown. We analyzed datasets from 5,306 participants across 15 countries (7 Latin American and Caribbean countries (LAC) and 8 non-LAC countries). Based on higher-order interactions, we developed a brain-age gap deep learning architecture for functional magnetic resonance imaging (2,953) and electroencephalography (2,353). The datasets comprised healthy controls and individuals with mild cognitive impairment, Alzheimer disease and behavioral variant frontotemporal dementia. LAC models evidenced older brain ages (functional magnetic resonance imaging: mean directional error = 5.60, root mean square error (r.m.s.e.) = 11.91; electroencephalography: mean directional error = 5.34, r.m.s.e. = 9.82) associated with frontoposterior networks compared with non-LAC models. Structural socioeconomic inequality, pollution and health disparities were influential predictors of increased brain-age gaps, especially in LAC (R² = 0.37, F² = 0.59, r.m.s.e. = 6.9). An ascending brain-age gap from healthy controls to mild cognitive impairment to Alzheimer disease was found. In LAC, we observed larger brain-age gaps in females in control and Alzheimer disease groups compared with the respective males. The results were not explained by variations in signal quality, demographics or acquisition methods. These findings provide a quantitative framework capturing the diversity of accelerated brain aging.

Few studies have validated the Spanish-language version of the Montreal Cognitive Assessment (MoCA-S) test in Latin American populations. To evaluate the psychometric properties and discriminant validity of the MoCA-S in elderly patients in Santiago de Chile. 172 individuals were grouped according to their clinical diagnosis based on the Clinical Dementia Rating (CDR) scale as follows: amnestic mild cognitive impairment (aMCI; n±24), non-amnestic MCI (naMCI; n±24), mild dementia (n±20), and cognitively normal (n±104). Participants were evaluated with both the MoCA-S and the Mini-Mental State Examination (MMSE) to determine the discriminant validity of the MoCA-S. Mean age and years of schooling were 73±6 and 11±4 years, respectively, with no significant intergroup differences. The MoCA-S displayed good internal consistency (Cronbach's α: 0.772), high inter-rater reliability (Spearman correlation coefficient: 0.846; P<.01), and high intra-rater reliability (test-retest reliability coefficient: 0.922; P<.001). The MoCA-S was found to be an effective and valid test for detecting aMCI (AUC±0.903) and mild dementia (AUC±0.957); its effectiveness for detecting naMCI was lower (AUC±0.629). The optimal cut-off points for aMCI and mild dementia were<21 and<20, respectively, with sensitivity and specificity rates of 75% and 82% for aMCI and 90% and 86% for mild dementia. The level of education had a great impact on scores: as a result, 2 points were added for patients with less than 8 years of schooling and one point for patients with 8-12 years of schooling (MoCA-S1-2). The MoCA-S1-2 showed significantly greater discriminant validity than the MMSE for differentiating aMCI from dementia. The MoCA-S1-2 is a short, easy-to-use, and useful test for diagnosing aMCI and mild dementia.