Windows Into Human Health Through Wearables Data Analytics.
Current opinion in biomedical engineering
2019; 9: 28–46
Glucotypes reveal new patterns of glucose dysregulation.
2018; 16 (7): e2005143
Background: Wearable sensors (wearables) have been commonly integrated into a wide variety of commercial products and are increasingly being used to collect and process raw physiological parameters into salient digital health information. The data collected by wearables are currently being investigated across a broad set of clinical domains and patient populations. There is significant research occurring in the domain of algorithm development, with the aim of translating raw sensor data into fitness- or health-related outcomes of interest for users, patients, and health care providers.Objectives: The aim of this review is to highlight a selected group of fitness- and health-related indicators from wearables data and to describe several algorithmic approaches used to generate these higher order indicators.Methods: A systematic search of the Pubmed database was performed with the following search terms (number of records in parentheses): Fitbit algorithm (18), Apple Watch algorithm (3), Garmin algorithm (5), Microsoft Band algorithm (8), Samsung Gear algorithm (2), Xiaomi MiBand algorithm (1), Huawei Band (Watch) algorithm (2), photoplethysmography algorithm (465), accelerometry algorithm (966), ECG algorithm (8287), continuous glucose monitor algorithm (343). The search terms chosen for this review are focused on algorithms for wearable devices that dominated the commercial wearables market between 2014-2017 and that were highly represented in the biomedical literature. A second set of search terms included categories of algorithms for fitness-related and health-related indicators that are commonly used in wearable devices (e.g. accelerometry, PPG, ECG). These papers covered the following domain areas: fitness; exercise; movement; physical activity; step count; walking; running; swimming; energy expenditure; atrial fibrillation; arrhythmia; cardiovascular; autonomic nervous system; neuropathy; heart rate variability; fall detection; trauma; behavior change; diet; eating; stress detection; serum glucose monitoring; continuous glucose monitoring; diabetes mellitus type 1; diabetes mellitus type 2. All studies uncovered through this search on commercially available device algorithms and pivotal studies on sensor algorithm development were summarized, and a summary table was constructed using references generated by the literature review as described (Table 1).Conclusions: Wearable health technologies aim to collect and process raw physiological or environmental parameters into salient digital health information. Much of the current and future utility of wearables lies in the signal processing steps and algorithms used to analyze large volumes of data. Continued algorithmic development and advances in machine learning techniques will further increase analytic capabilities. In the context of these advances, our review aims to highlight a range of advances in fitness- and other health-related indicators provided by current wearable technologies.
View details for DOI 10.1016/j.cobme.2019.01.001
View details for PubMedID 31832566
Personal Omics for Precision Health
2018; 122 (9): 1169–71
Cellular Decision Making by Non-Integrative Processing of TLR Inputs
2017; 19 (1): 125-135
Diabetes is an increasing problem worldwide; almost 30 million people, nearly 10% of the population, in the United States are diagnosed with diabetes. Another 84 million are prediabetic, and without intervention, up to 70% of these individuals may progress to type 2 diabetes. Current methods for quantifying blood glucose dysregulation in diabetes and prediabetes are limited by reliance on single-time-point measurements or on average measures of overall glycemia and neglect glucose dynamics. We have used continuous glucose monitoring (CGM) to evaluate the frequency with which individuals demonstrate elevations in postprandial glucose, the types of patterns, and how patterns vary between individuals given an identical nutrient challenge. Measurement of insulin resistance and secretion highlights the fact that the physiology underlying dysglycemia is highly variable between individuals. We developed an analytical framework that can group individuals according to specific patterns of glycemic responses called "glucotypes" that reveal heterogeneity, or subphenotypes, within traditional diagnostic categories of glucose regulation. Importantly, we found that even individuals considered normoglycemic by standard measures exhibit high glucose variability using CGM, with glucose levels reaching prediabetic and diabetic ranges 15% and 2% of the time, respectively. We thus show that glucose dysregulation, as characterized by CGM, is more prevalent and heterogeneous than previously thought and can affect individuals considered normoglycemic by standard measures, and specific patterns of glycemic responses reflect variable underlying physiology. The interindividual variability in glycemic responses to standardized meals also highlights the personal nature of glucose regulation. Through extensive phenotyping, we developed a model for identifying potential mechanisms of personal glucose dysregulation and built a webtool for visualizing a user-uploaded CGM profile and classifying individualized glucose patterns into glucotypes.
View details for PubMedID 30040822
Noise Induces Hopping between NF-kappa B Entrainment Modes
2016; 3 (6): 532-?
Cells receive a multitude of signals from the environment, but how they process simultaneous signaling inputs is not well understood. Response to infection, for example, involves parallel activation of multiple Toll-like receptors (TLRs) that converge on the nuclear factor κB (NF-κB) pathway. Although we increasingly understand inflammatory responses for isolated signals, it is not clear how cells process multiple signals that co-occur in physiological settings. We therefore examined a bacterial infection scenario involving co-stimulation of TLR4 and TLR2. Independent stimulation of these receptors induced distinct NF-κB dynamic profiles, although surprisingly, under co-stimulation, single cells continued to show ligand-specific dynamic responses characteristic of TLR2 or TLR4 signaling rather than a mixed response, comprising a cellular decision that we term "non-integrative" processing. Iterating modeling and microfluidic experiments revealed that non-integrative processing occurred through interaction of switch-like NF-κB activation, receptor-specific processing timescales, cell-to-cell variability, and TLR cross-tolerance mediated by multilayer negative feedback.
View details for DOI 10.1016/j.celrep.2017.03.027
View details for Web of Science ID 000398231800011
View details for PubMedID 28380352
Digital signaling decouples activation probability and population heterogeneity
Oscillations and noise drive many processes in biology, but how both affect the activity of the transcription factor nuclear factor κB (NF-κB) is not understood. Here, we observe that when NF-κB oscillations are entrained by periodic tumor necrosis factor (TNF) inputs in experiments, NF-κB exhibits jumps between frequency modes, a phenomenon we call "cellular mode-hopping." By comparing stochastic simulations of NF-κB oscillations to deterministic simulations conducted inside and outside the chaotic regime of parameter space, we show that noise facilitates mode-hopping in all regimes. However, when the deterministic system is driven by chaotic dynamics, hops between modes are erratic and short-lived, whereas in experiments, the system spends several periods in one entrainment mode before hopping and rarely visits more than two modes. The experimental behavior matches our simulations of noise-induced mode-hopping outside the chaotic regime. We suggest that mode-hopping is a mechanism by which different NF-κB-dependent genes under frequency control can be expressed at different times.
View details for DOI 10.1016/j.cels.2016.11.014
View details for Web of Science ID 000395782800008
View details for PubMedID 28009264
Noise Facilitates Transcriptional Control under Dynamic Inputs
2015; 160 (3): 381-392
Digital signaling enhances robustness of cellular decisions in noisy environments, but it is unclear how digital systems transmit temporal information about a stimulus. To understand how temporal input information is encoded and decoded by the NF-κB system, we studied transcription factor dynamics and gene regulation under dose- and duration-modulated inflammatory inputs. Mathematical modeling predicted and microfluidic single-cell experiments confirmed that integral of the stimulus (or area, concentration × duration) controls the fraction of cells that activate NF-κB in the population. However, stimulus temporal profile determined NF-κB dynamics, cell-to-cell variability, and gene expression phenotype. A sustained, weak stimulation lead to heterogeneous activation and delayed timing that is transmitted to gene expression. In contrast, a transient, strong stimulus with the same area caused rapid and uniform dynamics. These results show that digital NF-κB signaling enables multidimensional control of cellular phenotype via input profile, allowing parallel and independent control of single-cell activation probability and population heterogeneity.
View details for DOI 10.7554/eLife.08931
View details for Web of Science ID 000363799000001
View details for PubMedID 26488364
View details for PubMedCentralID PMC4608393
High-throughput microfluidic single-cell analysis pipeline for studies of signaling dynamics
2014; 9 (7): 1713-1726
Cells must respond sensitively to time-varying inputs in complex signaling environments. To understand how signaling networks process dynamic inputs into gene expression outputs and the role of noise in cellular information processing, we studied the immune pathway NF-κB under periodic cytokine inputs using microfluidic single-cell measurements and stochastic modeling. We find that NF-κB dynamics in fibroblasts synchronize with oscillating TNF signal and become entrained, leading to significantly increased NF-κB oscillation amplitude and mRNA output compared to non-entrained response. Simulations show that intrinsic biochemical noise in individual cells improves NF-κB oscillation and entrainment, whereas cell-to-cell variability in NF-κB natural frequency creates population robustness, together enabling entrainment over a wider range of dynamic inputs. This wide range is confirmed by experiments where entrained cells were measured under all input periods. These results indicate that synergy between oscillation and noise allows cells to achieve efficient gene expression in dynamically changing signaling environments.
View details for DOI 10.1016/j.cell.2015.01.013
View details for Web of Science ID 000349208000006
View details for PubMedID 25635454
Time-dependent analysis of dynamic processes in single live cells is a revolutionary technique for the quantitative studies of signaling networks. Here we describe an experimental pipeline and associated protocol that incorporate microfluidic cell culture, precise stimulation of cells with signaling molecules or drugs, live-cell microscopy, computerized cell tracking, on-chip staining of key proteins and subsequent retrieval of cells for high-throughput gene expression analysis using microfluidic quantitative PCR (qPCR). Compared with traditional culture dish approaches, this pipeline enhances experimental precision and throughput by orders of magnitude and introduces much-desired new capabilities in cell and fluid handling, thus representing a major step forward in dynamic single-cell analysis. A combination of microfluidic membrane valves, automation and a streamlined protocol now enables a single researcher to generate 1 million data points on single-cell protein localization within 1 week, in various cell types and densities, under 48 predesigned experimental conditions selected from different signaling molecules or drugs, their doses, timings and combinations.
View details for DOI 10.1038/nprot.2014.120
View details for Web of Science ID 000338777400013
View details for PubMedID 24967621