diabetes risk factors
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Diabetologia ◽  
2021 ◽  
Author(s):  
Milana A. Bochkur Dratver ◽  
Juliana Arenas ◽  
Tanayott Thaweethai ◽  
Chu Yu ◽  
Kaitlyn James ◽  
...  

2021 ◽  
Author(s):  
Mohamed Saleh ◽  
Joon Young Kim ◽  
Christine March ◽  
Nour Gebara ◽  
Silva Arslanian

Diabetes ◽  
2021 ◽  
Vol 70 (Supplement 1) ◽  
pp. 223-OR
Author(s):  
ANDREA LUK ◽  
XINGE ZHANG ◽  
ERIK FUNG ◽  
HONGJIANG WU ◽  
ERIC S. LAU ◽  
...  

Diabetes ◽  
2021 ◽  
Vol 70 (Supplement 1) ◽  
pp. 891-P
Author(s):  
VALERIA HIRSCHLER ◽  
CLAUDIO D. GONZALEZ ◽  
CLAUDIA MOLINARI ◽  
SILIVA LAPERTOSA ◽  

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Xiue Gao ◽  
Wenxue Xie ◽  
Zumin Wang ◽  
Bo Chen ◽  
Shengbin Zhou

Diabetes mellitus is a disease that has reached epidemic proportions globally in recent years. Consequently, the prevention and treatment of diabetes have become key social challenges. Most of the research on diabetes risk factors has focused on correlation analysis with little investigation into the causality of these risk factors. However, understanding the causality is also essential to preventing the disease. In this study, a causal discovery method for diabetes risk factors was developed based on an improved functional causal likelihood (IFCL) model. Firstly, the issue of excessive redundant and false edges in functional causal likelihood structures was resolved through the construction of an IFCL model using an adjustment threshold value. On this basis, an IFCL-based causal discovery algorithm was designed, and a simulation experiment was performed with the developed algorithm. The experimental results revealed that the causal structure generated using a dataset with a sample size of 2000 provided more information than that produced using a dataset with a sample size of 768. In addition, the causal structures obtained with the developed algorithm had fewer redundant and false edges. The following six causal relationships were identified: insulin→plasma glucose concentration, plasma glucose concentration→body mass index (BMI), triceps skin fold thickness→BMI and age, diastolic blood pressure→BMI, and number of times pregnant→age. Furthermore, the reasonableness of these causal relationships was investigated. The algorithm developed in this study enables the discovery of causal relationships among various diabetes risk factors and can serve as a reference for future causality studies on diabetes risk factors.


2021 ◽  
Vol 39 (Supplement 1) ◽  
pp. e121
Author(s):  
Silvio Paffer Filho ◽  
Matheus Toscano Paffer ◽  
Pedro Toscano Paffer

2021 ◽  
Author(s):  
Patty Kwan ◽  
Jonathan Watts ◽  
Jamie Michelle Prudencio ◽  
Lawrence Chu ◽  
Danielle Erika Co ◽  
...  

2021 ◽  
Vol 10 (12) ◽  
pp. 4471
Author(s):  
MuffarahH Alharthi ◽  
MagajiG Taura ◽  
AbdullahM AL-Shahrani ◽  
MohannadM Alamri ◽  
AbdullahM Alshahrani ◽  
...  

2020 ◽  
Vol 229 ◽  
pp. 165
Author(s):  
Silvio Paffer ◽  
Matheus Paffer ◽  
Pedro Paffer ◽  
Tatiana Paffer

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