scholarly journals The trends and the risk of type 1 diabetes over the past 40 years: an analysis by birth cohorts and by parental migration background in Sweden

BMJ Open ◽  
2013 ◽  
Vol 3 (10) ◽  
pp. e003418 ◽  
Author(s):  
Hozan Ismael Hussen ◽  
Martina Persson ◽  
Tahereh Moradi
2015 ◽  
Vol 169 (12) ◽  
pp. e153759 ◽  
Author(s):  
Maria C. Magnus ◽  
Sjurdur F. Olsen ◽  
Charlotta Granström ◽  
Geir Joner ◽  
Torild Skrivarhaug ◽  
...  

2015 ◽  
Vol 173 (5) ◽  
pp. R165-R183 ◽  
Author(s):  
Mohsen Khosravi-Maharlooei ◽  
Ensiyeh Hajizadeh-Saffar ◽  
Yaser Tahamtani ◽  
Mohsen Basiri ◽  
Leila Montazeri ◽  
...  

Over the past decades, tremendous efforts have been made to establish pancreatic islet transplantation as a standard therapy for type 1 diabetes. Recent advances in islet transplantation have resulted in steady improvements in the 5-year insulin independence rates for diabetic patients. Here we review the key challenges encountered in the islet transplantation field which include islet source limitation, sub-optimal engraftment of islets, lack of oxygen and blood supply for transplanted islets, and immune rejection of islets. Additionally, we discuss possible solutions for these challenges.


2013 ◽  
Vol 51 (1) ◽  
pp. R1-R13 ◽  
Author(s):  
Francesco Maria Egro

A series of studies have reported a constant global rise in the incidence of type 1 diabetes. Epidemiological and immunological studies have demonstrated that environmental factors may influence the pathogenesis, leading to a cell-mediated pancreatic β-cell destruction associated with humoral immunity. The search for the triggering factor(s) has been going on for the past century, and yet they are still unknown. This review provides an overview of some of the most well-known theories found in the literature: hygiene, viral, vitamin D deficiency, breast milk and cow's milk hypotheses. Although the hygiene hypothesis appears to be the most promising, positive evidence from animal, human and epidemiological studies precludes us from completely discarding any of the other hypotheses. Moreover, due to contrasting evidence in the literature, a single factor is unlikely to cause an increase in the incidence of diabetes all over the world, which suggests that a multifactorial process might be involved. Although the immunological mechanisms are still unclear, there seems to be some overlap between the various hypotheses. It is thought that the emphasis should be shifted from a single to a multifactorial process and that perhaps the ‘balance shift’ model should be considered as a possible explanation for the rise in the incidence of type 1 diabetes.


Diabetes Care ◽  
2017 ◽  
Vol 40 (7) ◽  
pp. 920-927 ◽  
Author(s):  
Nicolai A. Lund-Blix ◽  
Stine Dydensborg Sander ◽  
Ketil Størdal ◽  
Anne-Marie Nybo Andersen ◽  
Kjersti S. Rønningen ◽  
...  

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3593
Author(s):  
Lyvia Biagi ◽  
Arthur Bertachi ◽  
Marga Giménez ◽  
Ignacio Conget ◽  
Jorge Bondia ◽  
...  

The time spent in glucose ranges is a common metric in type 1 diabetes (T1D). As the time in one day is finite and limited, Compositional Data (CoDa) analysis is appropriate to deal with times spent in different glucose ranges in one day. This work proposes a CoDa approach applied to glucose profiles obtained from six T1D patients using continuous glucose monitor (CGM). Glucose profiles of 24-h and 6-h duration were categorized according to the relative interpretation of time spent in different glucose ranges, with the objective of presenting a probabilistic model of prediction of category of the next 6-h period based on the category of the previous 24-h period. A discriminant model for determining the category of the 24-h periods was obtained, achieving an average above 94% of correct classification. A probabilistic model of transition between the category of the past 24-h of glucose to the category of the future 6-h period was obtained. Results show that the approach based on CoDa is suitable for the categorization of glucose profiles giving rise to a new analysis tool. This tool could be very helpful for patients, to anticipate the occurrence of potential adverse events or undesirable variability and for physicians to assess patients’ outcomes and then tailor their therapies.


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