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Query: UMLS:C0011854 (type 1 diabetes)
 20,749 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

Minimally or noninvasive continuous glucose monitors estimate plasma glucose from compartments alternative to blood, and may revolutionize the management of diabetes. However, the accuracy of current devices is still poor and it may partly depend on low performance of the implemented calibration algorithm. Here, a new adaptive calibration algorithm based on a population local-model-based intercompartmental glucose dynamic model is proposed. The novelty consists in the adaptation of data normalization parameters in real time to estimate and compensate patient's sensitivity variations. Adaptation is performed to minimize mean absolute relative deviation at the calibration points with a time window forgetting strategy. Four calibrations are used: preprandial and 1.5 h postprandial at two different meals. Two databases are used for validation: 1) a 9-h CGMS Gold (Medtronic, Northridge, USA) time series with paired reference glucose values from a clinical study in 17 subjects with type 1 diabetes; 2) data from 30 virtual patients (UVa simulator, Virginia, USA), where inter- and intrasubject variability of sensor's sensitivity were simulated. Results show how the adaptation of the normalization parameters improves the performance of the calibration algorithm since it counteracts sensor sensitivity variations. This improvement is more evident in one-week simulations.
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PMID:Adaptive calibration algorithm for plasma glucose estimation in continuous glucose monitoring. 2459 55

Pathogenesis of type 1 diabetes is multi-faceted, including, autoimmunity, genetics and environment. Autoimmunity directed against pancreatic islet cells results in slowly progressive selective beta-cell destruction ("Primary autoimmune insulitis"), culminating over years in clinically manifested insulin-dependent diabetes mellitus (IDDM). Circulating serum autoantibodies directed against the endocrine cells of the islets of Langerhans (Islet cell autoantibodies - ICAb) are an important hallmark of this disease. Assays for islet cell autoantibodies have facilitated the investigation and understanding of several facets in the pathogenesis of autoimmune diabetes. Their applications have extended into clinical practice and have opened new avenues for early preclinical prediction and preventive prophylaxis in IDDM/type 1 DM. Recently, surprisingly, differences in insulin content between T1DM islets, as well as, 'patchy' or 'lobular' destruction of islets have been described. These unique pathobiological phenomena, suggest that beta cell destruction may not always be inexorable and inevitably complete/total, and thus raise hopes for possible therapeutic interruption of beta cell autoimmunity - destruction and cure of type 1 diabetes. "Recurrent or secondary autoimmune insulitis" refers to the rapid reappearance of islet cell autoantibodies post pancreas transplant, and selective islet beta cell destruction in the grafted pancreas [never forgetting or "anamnestic" beta cell destructive memory], in the absence of any graft pancreas rejection [monozygotic twin to twin transplantation]. The one definite environmental factor is congenital rubella, because of which a subset of children subsequently develop type 1 diabetes. The putative predisposing factors are viruses, gluten and cow's milk. The putative protective factors include gut flora, helminths, viral infections, and Vitamin D. Prevention of T1DM can include: Primary prevention strategies before the development of autoantibodies and Secondary prevention regimens after autoantibody development. Once islet cell autoantibodies have developed, the goal is to establish a therapeutic regimen to preserve at least 90% of the beta cells, and prevent the development of hyperglycaemia. The targets for T1DM reversal should include autoimmunity, beta cell regeneration and protection of beta cell mass. Anti-CD3 teplizumab and anti-CD3 otelixizumab have been shown to provide C-peptide preservation. The unanswered questions in diabetes research include elimination of autoimmune memory responses, reestablishment of immune self-tolerance, and mechanisms of disease initiation.
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PMID:Type 1 diabetes pathogenesis - Prevention??? 2594 54

To provide insight into poorly understood diabetes self-management among emerging adults with type 1 diabetes (TID) experiencing transitions, this study described their diabetes self-management-related habits, routines, and disruptions as well as explored relationships among habits and routines. A qualitative study, guided by critical incidence technique, was conducted. Participants were asked to describe situations when they did and did not check blood glucose, administer insulin, eat meals, and exercise as planned. They were also asked to describe activities in a typical day and in association with diabetes self-management. Content analysis with a priori definitions of habits and routines was performed. Participants described diabetes self-management-related transitional disruption as forgetting and disorder. They described habits associated with checking a blood glucose, giving an insulin dose, eating a meal, and initiating exercise. They described routines in association with meals, exercise, and overall diabetes management. These findings provide information on variables to target in intervention research.
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PMID:Habits and Routines during Transitions among Emerging Adults with Type 1 Diabetes. 3160 10

In type 1 diabetes, diurnal activity routines are influential factors in insulin dose calculations. Bolus advisors have been developed to more accurately suggest doses of meal-related insulin based on carbohydrate intake, according to pre-set insulin to carbohydrate levels and insulin sensitivity factors. These parameters can be varied according to the time of day and their optimal setting relies on identifying the daily time periods of routines accurately. The main issues with reporting and adjustments of daily activity routines are the reliance on self-reporting which is prone to inaccuracy and within bolus calculators, the keeping of default settings for daily time periods, such as within insulin pumps, glucose meters, and mobile applications. Moreover, daily routines are subject to change over periods of time which could go unnoticed. Hence, forgetting to change the daily time periods in the bolus calculator could contribute to sub-optimal self-management. In this paper, these issues are addressed by proposing a data-driven model for identification of diabetes diurnal patterns based on self-monitoring data. The model uses time-series clustering to achieve a meaningful separation of the patterns which is then used to identify the daily time periods and to advise of any time changes required. Further improvements in bolus advisor settings are proposed to include week/weekend or even modifiable daily time settings. The proposed model provides a quick, granular, more accurate, and personalized daily time setting profile while providing a more contextual perspective to glycemic pattern identification to both patients and clinicians.
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PMID:Intelligent Data-Driven Model for Diabetes Diurnal Patterns Analysis. 3209 21