Prediction of Mild Cognitive Impairment Status: Pilot Study of Machine Learning Models Based on Longitudinal Data From Fitness Trackers.

BACKGROUND: Early signs of Alzheimer disease (AD) are difficult to detect, causing diagnoses to be significantly delayed to time points when brain damage has already occurred and current experimental treatments have little effect on slowing disease progression. Tracking cognitive decline at early stages is critical for patients to make lifestyle changes and consider new and experimental therapies. Frequently studied biomarkers are invasive and costly and are limited for predicting conversion from normal to mild cognitive impairment (MCI). OBJECTIVE: This study aimed to use data collected from fitness trackers to predict MCI status. METHODS: In this pilot study, fitness trackers were worn by 20 participants: 12 patients with MCI and 8 age-matched controls. We collected physical activity, heart rate, and sleep data from each participant for up to 1 month and further developed a machine learning model to predict MCI status. RESULTS: Our machine learning model was able to perfectly separate between MCI and controls (area under the curve=1.0). The top predictive features from the model included peak, cardio, and fat burn heart rate zones; resting heart rate; average deep sleep time; and total light activity time. CONCLUSIONS: Our results suggest that a longitudinal digital biomarker differentiates between controls and patients with MCI in a very cost-effective and noninvasive way and hence may be very useful for identifying patients with very early AD who can benefit from clinical trials and new, disease-modifying therapies.

CVm6A: A Visualization and Exploration Database for m6As in Cell Lines.

N6-methyladenosine (m6A) has been identified in various biological processes and plays important regulatory functions in diverse cells. However, there is still no visualization database for exploring global m6A patterns across cell lines. Here we collected all available MeRIP-Seq and m6A-CLIP-Seq datasets from public databases and identified 340,950 and 179,201 m6A peaks dependent on 23 human and eight mouse cell lines respectively. Those m6A peaks were further classified into mRNA and lncRNA groups. To better understand the potential function of m6A, we then mapped m6A peaks in different subcellular components and gene regions. Among those human m6A modification, 190,050 and 150,900 peaks were identified in cancer and non-cancer cells, respectively. Finally, all results were integrated and imported into a visualized cell-dependent m6A database CVm6A. We believe the specificity of CVm6A could significantly contribute to the research for the function and regulation of cell-dependent m6A modification in disease and development.