Using the NANA toolkit at home to predict older adults' future depression

Background: Depression is currently underdiagnosed among older adults. As part of the Novel Assessment of Nu-trition and Aging (NANA) validation study, 40 older adults self-reported their mood using a touchscreen computer over three, one-week periods. Here, we demonstrate the potential of these data to predict future depression status. Methods: We analysed data from the NANA validation study using a machine learning approach. We applied the least absolute shrinkage and selection operator with a logistic model to averages of six measures of mood, with depression status according to the Geriatric Depression Scale 10 weeks later as the outcome variable. We tested multiple values of the selection parameter in order to produce a model with low deviance. We used a cross-validation framework to avoid overspecialisation, and receiver operating characteristic curve (ROC) analysis to determine the quality of the fitted model. Results: The model we report contained coefficients for two variables: sadness and tiredness, as well as a constant. The cross-validated area under the ROC curve for this model was 0.88 (CI: 0.69–0.97). Limitations: While results are based on a small sample, the methodology for the selection of variables appears suitable for the problem at hand, suggesting promise for a wider study and ultimate deployment with older adults at increased risk of depression. Conclusions: We have identified self-reported scales of sadness and tiredness as sensitive measures which have the potential to predict future depression status in older adults, partially addressing the problem of underdiagnosis

Uncovering the heterogeneity and temporal complexity of neurodegenerative diseases with Subtype and Stage Inference

The heterogeneity of neurodegenerative diseases is a key confound to disease understanding and treatment development, as study cohorts typically include multiple phenotypes on distinct disease trajectories. Here we introduce a machine-learning technique\u2014Subtype and Stage Inference (SuStaIn)\u2014able to uncover data-driven disease phenotypes with distinct temporal progression patterns, from widely available cross-sectional patient studies. Results from imaging studies in two neurodegenerative diseases reveal subgroups and their distinct trajectories of regional neurodegeneration. In genetic frontotemporal dementia, SuStaIn identifies genotypes from imaging alone, validating its ability to identify subtypes; further the technique reveals within-genotype heterogeneity. In Alzheimer\u2019s disease, SuStaIn uncovers three subtypes, uniquely characterising their temporal complexity. SuStaIn provides fine-grained patient stratification, which substantially enhances the ability to predict conversion between diagnostic categories over standard models that ignore subtype (p = 7.18 7 10 124 ) or temporal stage (p = 3.96 7 10 125 ). SuStaIn offers new promise for enabling disease subtype discovery and precision medicine

The health status of the elderly in a St. Petersburg district: Results of the crystal project

Using the "Crystal" study as an example of an epidemiological study that investigates global health of the elderly, the potential of a comprehensive geriatric assessment in primary care is illustrated. The results of the first cross-sectional data collection are presented in this paper with emphasis on the global health picture of the elderly; the average age was 75.08 +/- 5.96 years. In our study population one out of four participants was a male. Elderly mostly have a normal nutritional status with an average BMI of 28.6 +/- 4.94 kg/m2. On average, each participant has 2 +/- 1.27 pathologies, often cardiovascular. One out of four presents anemia. One third has symptoms of depression. In 44.4% of the patients a mild cognitive impairment was revealed. A more severe cognitive deficit was found in 26.2% of elderly. Every other old person has difficulties to keep balance in supine position for more than 10 seconds. One out of four elderly is partially dependent in their daily activity. Comprehensive geriatric assessment has the potential to identify priority issues in elderly health care and to inspire a management strategy. Further research is needed to create a simple instruments and an effective model to identify real health care needs of the elderly and to improve the quality of care and the successful collaboration between geriatricians and general practitioners