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      Digital Resilience Biomarkers for Personalized Health Maintenance and Disease Prevention

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          Abstract

          Health maintenance and disease prevention strategies become increasingly prioritized with increasing health and economic burden of chronic, lifestyle-related diseases. A key element in these strategies is the empowerment of individuals to control their health. Self-measurement plays an essential role in achieving such empowerment. Digital measurements have the advantage of being measured non-invasively, passively, continuously, and in a real-world context. An important question is whether such measurement can sensitively measure subtle disbalances in the progression toward disease, as well as the subtle effects of, for example, nutritional improvement. The concept of resilience biomarkers, defined as the dynamic evaluation of the biological response to an external challenge, has been identified as a viable strategy to measure these subtle effects. In this review, we explore the potential of integrating this concept with digital physiological measurements to come to digital resilience biomarkers. Additionally, we discuss the potential of wearable, non-invasive, and continuous measurement of molecular biomarkers. These types of innovative measurements may, in the future, also serve as a digital resilience biomarker to provide even more insight into the personal biological dynamics of an individual. Altogether, digital resilience biomarkers are envisioned to allow for the measurement of subtle effects of health maintenance and disease prevention strategies in a real-world context and thereby give personalized feedback to improve health.

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          An Overview of Heart Rate Variability Metrics and Norms

          Healthy biological systems exhibit complex patterns of variability that can be described by mathematical chaos. Heart rate variability (HRV) consists of changes in the time intervals between consecutive heartbeats called interbeat intervals (IBIs). A healthy heart is not a metronome. The oscillations of a healthy heart are complex and constantly changing, which allow the cardiovascular system to rapidly adjust to sudden physical and psychological challenges to homeostasis. This article briefly reviews current perspectives on the mechanisms that generate 24 h, short-term (~5 min), and ultra-short-term (<5 min) HRV, the importance of HRV, and its implications for health and performance. The authors provide an overview of widely-used HRV time-domain, frequency-domain, and non-linear metrics. Time-domain indices quantify the amount of HRV observed during monitoring periods that may range from ~2 min to 24 h. Frequency-domain values calculate the absolute or relative amount of signal energy within component bands. Non-linear measurements quantify the unpredictability and complexity of a series of IBIs. The authors survey published normative values for clinical, healthy, and optimal performance populations. They stress the importance of measurement context, including recording period length, subject age, and sex, on baseline HRV values. They caution that 24 h, short-term, and ultra-short-term normative values are not interchangeable. They encourage professionals to supplement published norms with findings from their own specialized populations. Finally, the authors provide an overview of HRV assessment strategies for clinical and optimal performance interventions.
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            Insulin sensitivity indices obtained from oral glucose tolerance testing: comparison with the euglycemic insulin clamp.

            Several methods have been proposed to evaluate insulin sensitivity from the data obtained from the oral glucose tolerance test (OGTT). However, the validity of these indices has not been rigorously evaluated by comparing them with the direct measurement of insulin sensitivity obtained with the euglycemic insulin clamp technique. In this study, we compare various insulin sensitivity indices derived from the OGTT with whole-body insulin sensitivity measured by the euglycemic insulin clamp technique. In this study, 153 subjects (66 men and 87 women, aged 18-71 years, BMI 20-65 kg/m2) with varying degrees of glucose tolerance (62 subjects with normal glucose tolerance, 31 subjects with impaired glucose tolerance, and 60 subjects with type 2 diabetes) were studied. After a 10-h overnight fast, all subjects underwent, in random order, a 75-g OGTT and a euglycemic insulin clamp, which was performed with the infusion of [3-3H]glucose. The indices of insulin sensitivity derived from OGTT data and the euglycemic insulin clamp were compared by correlation analysis. The mean plasma glucose concentration divided by the mean plasma insulin concentration during the OGTT displayed no correlation with the rate of whole-body glucose disposal during the euglycemic insulin clamp (r = -0.02, NS). From the OGTT, we developed an index of whole-body insulin sensitivity (10,000/square root of [fasting glucose x fasting insulin] x [mean glucose x mean insulin during OGTT]), which is highly correlated (r = 0.73, P < 0.0001) with the rate of whole-body glucose disposal during the euglycemic insulin clamp. Previous methods used to derive an index of insulin sensitivity from the OGTT have relied on the ratio of plasma glucose to insulin concentration during the OGTT. Our results demonstrate the limitations of such an approach. We have derived a novel estimate of insulin sensitivity that is simple to calculate and provides a reasonable approximation of whole-body insulin sensitivity from the OGTT.
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              The ‘Trier Social Stress Test’ – A Tool for Investigating Psychobiological Stress Responses in a Laboratory Setting

              This paper describes a protocol for induction of moderate psychological stress in a laboratory setting and evaluates its effects on physiological responses. The 'Trier Social Stress Test' (TSST) mainly consists of an anticipation period (10 min) and a test period (10 min) in which the subjects have to deliver a free speech and perform mental arithmetic in front of an audience. In six independent studies this protocol has been found to induce considerable changes in the concentration of ACTH, cortisol (serum and saliva), GH, prolactin as well as significant increases in heart rate. As for salivary cortisol levels, the TSST reliably led to 2- to 4-fold elevations above baseline with similar peak cortisol concentrations. Studies are summarized in which TSST-induced cortisol increases elucidated some of the multiple variables contributing to the interindividual variation in adrenocortical stress responses. The results suggest that gender, genetics and nicotine consumption can influence the individual's stress responsiveness to psychological stress while personality traits showed no correlation with cortisol responses to TSST stimulation. From these data we conclude that the TSST can serve as a tool for psychobiological research.
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                Author and article information

                Contributors
                Journal
                Front Digit Health
                Front Digit Health
                Front. Digit. Health
                Frontiers in Digital Health
                Frontiers Media S.A.
                2673-253X
                22 January 2021
                2020
                : 2
                : 614670
                Affiliations
                [1] 1Department of Microbiology and Systems Biology, Netherlands Organization for Applied Scientific Research (TNO) , Zeist, Netherlands
                [2] 2Department of Environmental Modeling Sensing and Analysis, Netherlands Organization for Applied Scientific Research (TNO) , Utrecht, Netherlands
                [3] 3Department of Optics, Netherlands Organization for Applied Scientific Research (TNO) , Delft, Netherlands
                [4] 4Holst Center, Netherlands Organization for Applied Scientific Research (TNO) , Eindhoven, Netherlands
                Author notes

                Edited by: Raghav Sundar, National University Health System, Singapore

                Reviewed by: Grzegorz Bulaj, The University of Utah, United States; Aishwarya Bandla, National University of Singapore, Singapore

                *Correspondence: Willem van den Brink willem.vandenbrink@ 123456tno.nl

                This article was submitted to Personalized Medicine, a section of the journal Frontiers in Digital Health

                Article
                10.3389/fdgth.2020.614670
                8521930
                34713076
                5ec5cccd-13de-4d8f-a8c7-5dd07541fdac
                Copyright © 2021 van den Brink, Bloem, Ananth, Kanagasabapathi, Amelink, Bouwman, Gelinck, van Veen, Boorsma and Wopereis.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 06 October 2020
                : 09 December 2020
                Page count
                Figures: 2, Tables: 3, Equations: 0, References: 110, Pages: 13, Words: 10201
                Categories
                Digital Health
                Review

                digital biomarker,prevention,lifestyle intervention,optical sensing,wearable sensors,resilience

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