Data-driven health deficit assessment improves a frailty index's prediction of current cognitive status and future conversion to dementia: results from ADNI.

Frailty is a dementia risk factor commonly measured by a frailty index (FI). The standard procedure for creating an FI requires manually selecting health deficit items and lacks criteria for selection optimization. We hypothesized that refining the item selection using data-driven assessment improves sensitivity to cognitive status and future dementia conversion, and compared the predictive value of three FIs: a standard 93-item FI was created after selecting health deficit items according to standard criteria (FIs) from the ADNI database. A refined FI (FIr) was calculated by using a subset of items, identified using factor analysis of mixed data (FAMD)-based cluster analysis. We developed both FIs for the ADNI1 cohort (n = 819). We also calculated another standard FI (FIc) developed by Canevelli and coworkers. Results were validated in an external sample by pooling ADNI2 and ADNI-GO cohorts (n = 815). Cluster analysis yielded two clusters of subjects, which significantly (pFDR < .05) differed on 26 health items, which were used to compute FIr. The data-driven subset of items included in FIr covered a range of systems and included well-known frailty components, e.g., gait alterations and low energy. In prediction analyses, FIr outperformed FIs and FIc in terms of baseline cognition and future dementia conversion in the training and validation cohorts. In conclusion, the data show that data-driven health deficit assessment improves an FI's prediction of current cognitive status and future dementia, and suggest that the standard FI procedure needs to be refined when used for dementia risk assessment purposes.

A common brain network links development, aging, and vulnerability to disease.

Several theories link processes of development and aging in humans. In neuroscience, one model posits for instance that healthy age-related brain degeneration mirrors development, with the areas of the brain thought to develop later also degenerating earlier. However, intrinsic evidence for such a link between healthy aging and development in brain structure remains elusive. Here, we show that a data-driven analysis of brain structural variation across 484 healthy participants (8-85 y) reveals a largely--but not only--transmodal network whose lifespan pattern of age-related change intrinsically supports this model of mirroring development and aging. We further demonstrate that this network of brain regions, which develops relatively late during adolescence and shows accelerated degeneration in old age compared with the rest of the brain, characterizes areas of heightened vulnerability to unhealthy developmental and aging processes, as exemplified by schizophrenia and Alzheimer's disease, respectively. Specifically, this network, while derived solely from healthy subjects, spatially recapitulates the pattern of brain abnormalities observed in both schizophrenia and Alzheimer's disease. This network is further associated in our large-scale healthy population with intellectual ability and episodic memory, whose impairment contributes to key symptoms of schizophrenia and Alzheimer's disease. Taken together, our results suggest that the common spatial pattern of abnormalities observed in these two disorders, which emerge at opposite ends of the life spectrum, might be influenced by the timing of their separate and distinct pathological processes in disrupting healthy cerebral development and aging, respectively.