scholarly journals Development of a screening tool using electronic health records for undiagnosed Type 2 diabetes mellitus and impaired fasting glucose detection in the Slovenian population

2018 ◽  
Vol 35 (5) ◽  
pp. 640-649 ◽  
Author(s):  
G. Štiglic ◽  
P. Kocbek ◽  
L. Cilar ◽  
N. Fijačko ◽  
A. Stožer ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Electronic Health Records ◽  
Screening Tool ◽  
Fasting Glucose ◽  
Glucose Detection ◽  
Health Records ◽  
Slovenian Population

scholarly journals Epidemiology and determinants of non-diabetic hyperglycaemia and its conversion to type 2 diabetes mellitus, 2000–2015: cohort population study using UK electronic health records

BMJ Open ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. e040201 ◽  
Author(s):  
Rathi Ravindrarajah ◽  
David Reeves ◽  
Elizabeth Howarth ◽  
Rachel Meacock ◽  
Claudia Soiland-Reyes ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Electronic Health Records ◽  
Secondary Outcome ◽  
Health Records ◽  
Conversion Rates ◽  
Electronic Health ◽  
Over Time

ObjectivesTo study the characteristics of UK individuals identified with non-diabetic hyperglycaemia (NDH) and their conversion rates to type 2 diabetes mellitus (T2DM) from 2000 to 2015, using the Clinical Practice Research Datalink.DesignCohort study.SettingsUK primary Care Practices.ParticipantsElectronic health records identified 14 272 participants with NDH, from 2000 to 2015.Primary and secondary outcome measuresBaseline characteristics and conversion trends from NDH to T2DM were explored. Cox proportional hazards models evaluated predictors of conversion.ResultsCrude conversion was 4% within 6 months of NDH diagnosis, 7% annually, 13% within 2 years, 17% within 3 years and 23% within 5 years. However, 1-year conversion fell from 8% in 2000 to 4% in 2014. Individuals aged 45–54 were at the highest risk of developing T2DM (HR 1.20, 95% CI 1.15 to 1.25— compared with those aged 18–44), and the risk reduced with older age. A body mass index (BMI) above 30 kg/m2 was strongly associated with conversion (HR 2.02, 95% CI 1.92 to 2.13—compared with those with a normal BMI). Depression (HR 1.10, 95% CI 1.07 to 1.13), smoking (HR 1.07, 95% CI 1.03 to 1.11—compared with non-smokers) or residing in the most deprived areas (HR 1.17, 95% CI 1.11 to 1.24—compared with residents of the most affluent areas) was modestly associated with conversion.ConclusionAlthough the rate of conversion from NDH to T2DM fell between 2010 and 2015, this is likely due to changes over time in the cut-off points for defining NDH, and more people of lower diabetes risk being diagnosed with NDH over time. People aged 45–54, smokers, depressed, with high BMI and more deprived are at increased risk of conversion to T2DM.

Screening diabetes mellitus 2 based on electronic health records using temporal features

2016 ◽  
Vol 24 (2) ◽  
pp. 194-205 ◽  
Author(s):  
Angela Pimentel ◽  
André V Carreiro ◽  
Rogério T Ribeiro ◽  
Hugo Gamboa
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Electronic Health Records ◽  
Diabetic Patients ◽  
Health Records ◽  
Temporal Features ◽  
Electronic Health ◽  
Invasive Techniques

The prevalence of type 2 diabetes mellitus is increasing worldwide. Current methods of treating diabetes remain inadequate, and therefore, prevention with screening methods is the most appropriate process to reduce the burden of diabetes and its complications. We propose a new prognostic approach for type 2 diabetes mellitus based on electronic health records without using the current invasive techniques that are related to the disease (e.g. glucose level or glycated hemoglobin (HbA1c)). Our methodology is based on machine learning frameworks with data enrichment using temporal features. As as result our predictive model achieved an area under the receiver operating characteristics curve with a random forest classifier of 84.22 percent when including data information from 2009 to 2011 to predict diabetic patients in 2012, 83.19 percent when including temporal features, and 83.72 percent after applying temporal features and feature selection. We conclude that he pathology prediction is possible and efficient using the patient’s progression information over the years and without using the invasive techniques that are currently used for type 2 diabetes mellitus classification.

Circulating Netrin-1 as a Novel Biomarker for Impaired Fasting Glucose and Newly Diagnosed Type 2 Diabetes Mellitus

Diabetes ◽  
2018 ◽  
Vol 67 (Supplement 1) ◽  
pp. 1535-P ◽  
Author(s):  
HYE-IN JUNG ◽  
JAEHYUN BAE ◽  
EUGENE HAN ◽  
GYURI KIM ◽  
JI-YEON LEE ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Impaired Fasting Glucose ◽  
Fasting Glucose ◽  
Newly Diagnosed

scholarly journals Association of impaired fasting glucose and Type 2 Diabetes Mellitus with brain volume changes in Alzheimer’s Disease patients analyzed by MRI: a retrospective study

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e9801
Author(s):  
Weiwei Wang ◽  
Leongtim Wong ◽  
Lin Shi ◽  
Yishan Luo ◽  
Zhanhua Liang ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Glucose Level ◽  
Brain Volume ◽  
Fasting Glucose ◽  
Fasting Glucose Level ◽  
Volume Changes ◽  
Brain Volumes

Objectives Alzheimer’s disease (AD), impaired fasting glucose (IFG), and Type 2 diabetes mellitus (T2DM) were reported associated with smaller brain volumes. Nevertheless, the association of hyperglycemia with brain volume changes in AD patients remains unclear. To investigate this issue, structural magnetic resonance imaging was used to compare brain volumes among AD patients with different fasting glucose levels. Methods Eighty-five AD patients were divided into three groups based on their fasting glucose level as suggested by the American Diabetes Association: normal fasting glucose group (AD_NFG, n = 45), AD_IFG group (n = 15), and AD_T2DM group (n = 25). Sagittal 3D T1-weighted images were obtained to calculate the brain volume. Brain parenchyma and 33 brain structures were automatically segmented. Each regional volume was analyzed among groups. For regions with statistical significance, partial correlation analysis was used to evaluate their relationships with fasting glucose level, corrected for Mini-Mental State Examination score, age, education level, cholesterol, triglyceride, and blood pressure. Results Compared with the AD_IFG and AD_NFG groups, the volume of pons in AD_T2DM group was significantly smaller. Fasting glucose was negatively correlated with pontine volume. Conclusions T2DM may exacerbate pontine atrophy in AD patients, and fasting glucose level is associated with pontine volume.

scholarly journals Prevalence of newly diagnosed prediabetes, diabetes mellitus among the officers from Phuoc Long district of Binh Phuoc province

2021 ◽  
pp. 24-28
Author(s):  
Thanh Long Le ◽  
Trung Vinh Hoang
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Glucose Tolerance ◽  
Impaired Glucose Tolerance ◽  
Impaired Fasting Glucose ◽  
Fasting Glucose ◽  
Newly Diagnosed ◽  
Personal Communications

Objective: To evaluate the prevalence of newly diagnosed prediabetes, diabetes mellitus among the officers from Phuoc Long district of Binh Phuoc province. Subjects and methods: 268 personals communications service was examined the impaired fasting glucose (G0); impaired glucose tolerance (G2) anh HbA1c. Results: Prevalence of prediabetes, type 2 diabetes mellitus by G0, G2, HbA1c as follows 16,0%; 13,1%; 17,9% and 3,8%; 6,7%; 2,2%. Common prevalence of prediabetes in 26,9%; type 2 diabetes mellitus in 7,1%. Conclusion: Personal communications service from Phuoc Long district have percentage of prediabetes higher compared to type 2 diabetes mellitus which of prediabetes was diagnosed by HbA1c which takes up the highest percentage; diabetes mellitus was diagnosed by G2which takes up the highest percentage.

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