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Systematic review of the prospective association of daily step counts with risk of mortality, cardiovascular disease, and dysglycemia.
Hall, KS, Hyde, ET, Bassett, DR, Carlson, SA, Carnethon, MR, Ekelund, U, Evenson, KR, Galuska, DA, Kraus, WE, Lee, IM, et al
The international journal of behavioral nutrition and physical activity. 2020;17(1):78
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The health benefits of physical activity for people of all ages, fitness levels, and sociodemographic backgrounds are well-documented. The main aim of this study was to provide an updated description of the association between daily step counts and subsequent cardiovascular disease (CVD) morbidity or mortality, dysglycaemia, and all-cause mortality in adults and the patterns of these associations. This study is a systemic review of 17 studies from 13 different cohorts. Participants’ mean age ranged from 49.7 to 78.9 years with samples comprised of 46.9% female participants on average. Results showed that increasing steps per day is beneficial for health: taking more steps per day was associated with lower risk of all-cause mortality, and lower risk of CVD morbidity or mortality. These associations appear to hold across age, gender, and weight status. Authors conclude that this additional evidence will help guide meaningful volume targets that can be used for health care, education, and behavioural interventions, and potentially inform the development of public health guidelines for steps and health.
Abstract
BACKGROUND Daily step counts is an intuitive metric that has demonstrated success in motivating physical activity in adults and may hold potential for future public health physical activity recommendations. This review seeks to clarify the pattern of the associations between daily steps and subsequent all-cause mortality, cardiovascular disease (CVD) morbidity and mortality, and dysglycemia, as well as the number of daily steps needed for health outcomes. METHODS A systematic review was conducted to identify prospective studies assessing daily step count measured by pedometer or accelerometer and their associations with all-cause mortality, CVD morbidity or mortality, and dysglycemia (dysglycemia or diabetes incidence, insulin sensitivity, fasting glucose, HbA1c). The search was performed across the Medline, Embase, CINAHL, and the Cochrane Library databases from inception to August 1, 2019. Eligibility criteria included longitudinal design with health outcomes assessed at baseline and subsequent timepoints; defining steps per day as the exposure; reporting all-cause mortality, CVD morbidity or mortality, and/or dysglycemia outcomes; adults ≥18 years old; and non-patient populations. RESULTS Seventeen prospective studies involving over 30,000 adults were identified. Five studies reported on all-cause mortality (follow-up time 4-10 years), four on cardiovascular risk or events (6 months to 6 years), and eight on dysglycemia outcomes (3 months to 5 years). For each 1000 daily step count increase at baseline, risk reductions in all-cause mortality (6-36%) and CVD (5-21%) at follow-up were estimated across a subsample of included studies. There was no evidence of significant interaction by age, sex, health conditions or behaviors (e.g., alcohol use, smoking status, diet) among studies that tested for interactions. Studies examining dysglycemia outcomes report inconsistent findings, partially due to heterogeneity across studies of glycemia-related biomarker outcomes, analytic approaches, and sample characteristics. CONCLUSIONS Evidence from longitudinal data consistently demonstrated that walking an additional 1000 steps per day can help lower the risk of all-cause mortality, and CVD morbidity and mortality in adults, and that health benefits are present below 10,000 steps per day. However, the shape of the dose-response relation is not yet clear. Data are currently lacking to identify a specific minimum threshold of daily step counts needed to obtain overall health benefit.
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Glucotypes reveal new patterns of glucose dysregulation.
Hall, H, Perelman, D, Breschi, A, Limcaoco, P, Kellogg, R, McLaughlin, T, Snyder, M
PLoS biology. 2018;16(7):e2005143
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One in 10 individuals is affected by diabetes, a condition involving abnormal regulation of blood glucose. Currently, diabetes is assessed using single-time or average measurements of blood glucose, without consideration for how blood glucose fluctuates over time. This study used continuous glucose monitoring (CGM) technology to evaluate how blood glucose fluctuates in individuals over time. The authors found that many individuals considered nondiabetic by standard measures experienced frequent elevations in blood glucose levels into the pre-diabetic or diabetic range (15% and 2% of the time, respectively). The authors developed a model for determining the “glucotype” (low, moderate or severe variability) of an individual, a more comprehensive measure of glucose patterns than the standard tests currently used. The authors argue that CGM should become an important tool in early identification of those at risk for type 2 diabetes.
Abstract
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.