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Seminars in Perinatology Apr 2000After a brief history of the development of neonatal hypoglycemia, this review emphasizes the current approach to the anticipation, diagnosis, and management of the... (Review)
Review
After a brief history of the development of neonatal hypoglycemia, this review emphasizes the current approach to the anticipation, diagnosis, and management of the neonate with a low plasma glucose concentration. Current techniques for studying the neurophysiological and endocrine-metabolic effects of significant hypoglycemia provide new approaches for establishing relevant definitions of significant hypoglycemia, its prognosis, and pathogenesis. The inadequacy of glucose oxidase strips for screening, the definition of high-risk infants, new definitions for low plasma glucose concentrations, and their treatment are presented as well as the ability of the neonate to respond to significantly low glucose values. New data concerning the hereditary aspects of hyperinsulinemia (Glaser, this issue), hereditary defects in branched-chain amino acid, 3-methylglutaconic aciduria and mitochondrial betaoxidation, and degradation of fatty acids (Ozand, this issue), the role of glucose transporters (Vannucci and Vannucci, this issue), and the newer computed tomography and magnetic resonance imaging techniques (Kinnala, this issue) to study neonatal hypoglycemia are reviewed elsewhere in this issue.
Topics: Blood Glucose; Follow-Up Studies; Humans; Hypoglycemia; Infant, Newborn; Prognosis; Recurrence
PubMed: 10805169
DOI: 10.1053/sp.2000.6364 -
Journal of Medical Internet Research May 2019Diabetes mellitus is a chronic metabolic disorder that results in abnormal blood glucose (BG) regulations. The BG level is preferably maintained close to normality... (Review)
Review
BACKGROUND
Diabetes mellitus is a chronic metabolic disorder that results in abnormal blood glucose (BG) regulations. The BG level is preferably maintained close to normality through self-management practices, which involves actively tracking BG levels and taking proper actions including adjusting diet and insulin medications. BG anomalies could be defined as any undesirable reading because of either a precisely known reason (normal cause variation) or an unknown reason (special cause variation) to the patient. Recently, machine-learning applications have been widely introduced within diabetes research in general and BG anomaly detection in particular. However, irrespective of their expanding and increasing popularity, there is a lack of up-to-date reviews that materialize the current trends in modeling options and strategies for BG anomaly classification and detection in people with diabetes.
OBJECTIVE
This review aimed to identify, assess, and analyze the state-of-the-art machine-learning strategies and their hybrid systems focusing on BG anomaly classification and detection including glycemic variability (GV), hyperglycemia, and hypoglycemia in type 1 diabetes within the context of personalized decision support systems and BG alarm events applications, which are important constituents for optimal diabetes self-management.
METHODS
A rigorous literature search was conducted between September 1 and October 1, 2017, and October 15 and November 5, 2018, through various Web-based databases. Peer-reviewed journals and articles were considered. Information from the selected literature was extracted based on predefined categories, which were based on previous research and further elaborated through brainstorming.
RESULTS
The initial results were vetted using the title, abstract, and keywords and retrieved 496 papers. After a thorough assessment and screening, 47 articles remained, which were critically analyzed. The interrater agreement was measured using a Cohen kappa test, and disagreements were resolved through discussion. The state-of-the-art classes of machine learning have been developed and tested up to the task and achieved promising performance including artificial neural network, support vector machine, decision tree, genetic algorithm, Gaussian process regression, Bayesian neural network, deep belief network, and others.
CONCLUSIONS
Despite the complexity of BG dynamics, there are many attempts to capture hypoglycemia and hyperglycemia incidences and the extent of an individual's GV using different approaches. Recently, the advancement of diabetes technologies and continuous accumulation of self-collected health data have paved the way for popularity of machine learning in these tasks. According to the review, most of the identified studies used a theoretical threshold, which suffers from inter- and intrapatient variation. Therefore, future studies should consider the difference among patients and also track its temporal change over time. Moreover, studies should also give more emphasis on the types of inputs used and their associated time lag. Generally, we foresee that these developments might encourage researchers to further develop and test these systems on a large-scale basis.
Topics: Algorithms; Blood Glucose; Diabetes Mellitus, Type 1; Female; Humans; Machine Learning; Male
PubMed: 31042157
DOI: 10.2196/11030 -
Sensors (Basel, Switzerland) Jan 2017Diabetes has become a leading cause of death worldwide. Although there is no cure for diabetes, blood glucose monitoring combined with appropriate medication can enhance... (Review)
Review
Diabetes has become a leading cause of death worldwide. Although there is no cure for diabetes, blood glucose monitoring combined with appropriate medication can enhance treatment efficiency, alleviate the symptoms, as well as diminish the complications. For point-of-care purposes, continuous glucose monitoring (CGM) devices are considered to be the best candidates for diabetes therapy. This review focuses on current growth areas of CGM technologies, specifically focusing on subcutaneous implantable electrochemical glucose sensors. The superiority of CGM systems is introduced firstly, and then the strategies for fabrication of minimally-invasive and non-invasive CGM biosensors are discussed, respectively. Finally, we briefly outline the current status and future perspective for CGM systems.
Topics: Blood Glucose; Diabetes Mellitus; Humans; Monitoring, Physiologic; Point-of-Care Systems
PubMed: 28106820
DOI: 10.3390/s17010182 -
Journal of Diabetes Science and... Nov 2016In the present era of near-continuous glucose monitoring (CGM) and automated therapeutic closed-loop systems, measures of accuracy and of quality of glucose control need... (Review)
Review
In the present era of near-continuous glucose monitoring (CGM) and automated therapeutic closed-loop systems, measures of accuracy and of quality of glucose control need to be standardized for licensing authorities and to enable comparisons across studies and devices. Adequately powered, good quality, randomized, controlled studies are needed to assess the impact of different CGM devices on the quality of glucose control, workload, and costs. The additional effects of continuing glucose control on the general floor after the ICU stay also need to be investigated. Current algorithms need to be adapted and validated for CGM, including effects on glucose variability and workload. Improved collaboration within the industry needs to be encouraged because no single company produces all the necessary components for an automated closed-loop system. Combining glucose measurement with measurement of other variables in 1 sensor may help make this approach more financially viable.
Topics: Blood Glucose; Intensive Care Units; Monitoring, Physiologic
PubMed: 27170632
DOI: 10.1177/1932296816648713 -
Diabetes Care Jun 2009
Topics: Blood Glucose; Consensus Development Conferences as Topic; Diabetes Mellitus; Endocrinology; Humans; Hyperglycemia; Inpatients; Societies, Medical
PubMed: 19429873
DOI: 10.2337/dc09-9029 -
Physiological Reviews Jan 2003A new framework for understanding the control of feeding behavior, with special emphasis on the evolution of hunger, the initiation of feeding, and its dependence on... (Review)
Review
A new framework for understanding the control of feeding behavior, with special emphasis on the evolution of hunger, the initiation of feeding, and its dependence on patterns of blood glucose, is the subject of this review. A perspective on the current status and future directions of this search for a more complete understanding of the regulation of feeding behavior in laboratory rats and humans is presented including theoretical and experimental components. First, a historical perspective on the role of blood glucose in the control of feeding is presented. Next, the theoretical approaches that have been applied to the control of feeding and had a strong influence on experimental feeding research are summarized. This is followed by a statement and overview of a current theory that has emerged from studies of the role of transient declines in blood glucose in the control of meal initiation. The current working hypothesis that transient declines in blood glucose are endogenous metabolic patterns that are detected and recognized by the central nervous system and are mapped into meal initiation in rats and are correlated with meal requests in humans are then presented. Then, the experimental studies on meal initiation and its dependence on patterns of blood glucose, first in rats and then in humans, are reviewed in detail. Finally, the future directions of the work, limitations, and the implications for the understanding of the control of feeding behavior and the regulation of energy balance are discussed.
Topics: Animals; Blood Glucose; Feeding Behavior; Humans; Models, Biological; Models, Psychological
PubMed: 12506126
DOI: 10.1152/physrev.00019.2002 -
Journal of Diabetes Science and... Nov 2011Measurement of hemoglobin A1c (HbA1c) is considered the gold standard for monitoring chronic glycemia of diabetes patients. Hemoglobin A1c indicates an average of blood... (Review)
Review
Measurement of hemoglobin A1c (HbA1c) is considered the gold standard for monitoring chronic glycemia of diabetes patients. Hemoglobin A1c indicates an average of blood glucose levels over the past 3 months. Its close association with the risk for the development of long-term complications is well established. However, HbA1c does not inform patients about blood glucose values on a daily basis; therefore, frequent measurements of blood glucose levels are necessary for the day-to-day management of diabetes. Clinicians understand what HbA1c means and how it relates to glucose, but this is not the case with patients. Therefore, the translation of the HbA1c results into something more familiar to patients seemed a necessity. The scope of this article is to review the literature to search for enough scientific evidence to support the idea of a close relationship between HbA1c and mean blood glucose (MBG), and to justify the translation of HbA1c into something that reflects the MBG. Most studies confirm a close relationship between HbA1c and MBG, although different studies result in different linear equations. Factors affecting this relationship may limit the usefulness and applicability of a unique mathematical equation to all diabetes populations.
Topics: Blood Glucose; Diabetes Mellitus; Glycated Hemoglobin; Humans
PubMed: 22226280
DOI: 10.1177/193229681100500634 -
Journal of Diabetes Science and... Jul 2016The chaotic nature of blood glucose creates a formidable clinical challenge for diabetes healthcare. The recent discovery of recurrent endocrine cycles offers the...
BACKGROUND
The chaotic nature of blood glucose creates a formidable clinical challenge for diabetes healthcare. The recent discovery of recurrent endocrine cycles offers the advantage of advanced-prediction (proactive) health care.
METHODS
Historical studies covering 111 patients and 1 subject collected several months of glucose readings and their daily metrics. Phase portraits and phase analytics can detect recurrent metric cycles and test their ability to anticipate serious glycemic conditions.
RESULTS
Recurrent patterns were detected having a rate of ~7 days per complete cycle. Plots and risk models based on these cycles produced advanced alerts for acute glycemia, capturing greater than 96% of true-positive days with a 5% false-positive rate.
CONCLUSIONS
This method can be implemented graphically and functionally within a BG monitoring system to warn doctors and patients of impending serious glycemic levels.
Topics: Blood Glucose; Humans; Hyperglycemia; Logistic Models
PubMed: 26961975
DOI: 10.1177/1932296816637622 -
Oxidative Medicine and Cellular... 2020Glucagon, a hormone secreted by pancreatic alpha cells, contributes to the maintenance of normal blood glucose concentration by inducing hepatic glucose production in... (Review)
Review
Glucagon, a hormone secreted by pancreatic alpha cells, contributes to the maintenance of normal blood glucose concentration by inducing hepatic glucose production in response to declining blood glucose. However, glucagon hypersecretion contributes to the pathogenesis of type 2 diabetes. Moreover, diabetes is associated with relative glucagon undersecretion at low blood glucose and oversecretion at normal and high blood glucose. The mechanisms of such alpha cell dysfunctions are not well understood. This article reviews the genesis of alpha cell dysfunctions during the pathogenesis of type 2 diabetes and after the onset of type 1 and type 2 diabetes. It unravels a signaling pathway that contributes to glucose- or hydrogen peroxide-induced glucagon secretion, whose overstimulation contributes to glucagon dysregulation, partly through oxidative stress and reduced ATP synthesis. The signaling pathway involves phosphatidylinositol-3-kinase, protein kinase B, protein kinase C delta, non-receptor tyrosine kinase Src, and phospholipase C gamma-1. This knowledge will be useful in the design of new antidiabetic agents or regimens.
Topics: Blood Glucose; Glucagon; Humans
PubMed: 32774668
DOI: 10.1155/2020/3089139 -
Nutrition & Diabetes Mar 2022Existing evidence on the effects of glucose supplementation on cognitive performance appears inconclusive. Metabolic switching offers an approach to explain such...
Existing evidence on the effects of glucose supplementation on cognitive performance appears inconclusive. Metabolic switching offers an approach to explain such incoherent findings based on differences in cognitive functioning after fasting. We propose a new construct, cognitive glucose sensitivity (CGS), which quantifies individual performance gain due to glucose supplementation. We tested the hypothesis that the effects of glucose ingestion depend on CGS, cognitive task domain, and sex. In addition, the relationship between CGS and body mass index (BMI) was examined. Seventy-one participants (48 female) were tested in two conditions each (deprivation baseline vs. glucose supplementation), performing tasks from different cognitive domains (memory and executive functioning). We found significant evidence for a correlation of deprivation baseline performance and CGS across domains (Corsi-Block-Tapping Task: r = -0.57, p < 0.001; Go-No-Go Task: r = 0.39, p = 0.001; word list recall: r = -0.50, p < 0.001). Moreover, individual CGS differed significantly between tasks (p = 0.018). Only in men, BMI was significantly related to CGS in a word recall paradigm (r = 0.49, p = 0.017). Our findings support the notion that the effects of glucose depend on CGS, task domain, and sex. The effort to reduce performance impairment (short-term) might sacrifice independence from external glucose (long term), possibly via declining blood glucose regulation. Therefore, CGS could be regarded as a candidate to enhance our understanding of the etiology of unhealthy eating.
Topics: Blood Glucose; Cognition; Female; Glucose; Humans; Male
PubMed: 35288532
DOI: 10.1038/s41387-022-00191-6