scholarly journals Indian Diabetes Risk Score Helps to Distinguish Type 2 from Non-Type 2 Diabetes Mellitus (GDRC-3)

2011 ◽  
Vol 5 (2) ◽  
pp. 419-425 ◽  
Author(s):  
Kunal M. Sharma ◽  
Harish Ranjani ◽  
Ha Nguyen ◽  
Shuba Shetty ◽  
Manjula Datta ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Risk Score ◽  
Diabetes Risk ◽  
Diabetes Risk Score

scholarly journals Prediction of type 2 diabetes mellitus onset using simple logistic regression models

2020 ◽  
Author(s):  
Yochai Edlitz ◽  
Eran Segal
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Logistic Regression ◽  
Risk Score ◽  
Diabetes Risk ◽  
Blood Tests ◽  
Glutamyl Transferase ◽  
Diabetes Risk Score

Diabetes mellitus has a world death rate of 1.6 million (2016) of which Type 2 diabetes mellitus (T2DM) accounts for ~90% of all cases. Early detection of T2D high-risk patients can reduce the incidence of the disease through a change in lifestyle, diet, or medication. Since lower socio-demographics layers are more susceptible to T2D and might have limited resources for laboratory testing, there is a need for accurate prediction models based on non-laboratory parameters. Here, we analysed data of 44,879 non-diabetic, UK-Biobank participants at the ages 40-65 within a time frame of 7.3±2.3 years. We devise a non-laboratory prediction model for T2DM onset probability using sex, age, weight, height, waist, hips-circumferences, Waist-Hips Ratio (WHR) and Body-Mass Index (BMI). This model achieved an Area Under the Receiver Operating Curve (auROC) of 0.82 (0.79-0.84 95% CI) and an odds ratio (OR) between the top and lowest prevalence deciles of x42 (33-49). The logistic regression top predictive parameters are WHR with OR of 0.67 (0.49-0.88 95%CI) followed by BMI with OR of 0.53 (0.26-0.79). We further analyse the contribution of laboratory-based parameters and devise a blood-test model based on only five blood tests. In this model, we are using age, sex, Glycated Hemoglobin (HbA1c%), reticulocyte count, Gamma Glutamyl-Transferase, Triglycerides, and HDL cholesterol to predict T2D onset more accurately. This model achieves an auROC of 0.89 (0.87-0.92) and a deciles' OR of x59 (27-75). We also analysed a model that included genotyping data and other environmental factors and found that it did not provide further benefit over the five-blood-tests model. Our models outperform the current state of the art, non-laboratory, Finnish Diabetes Risk Score and the German Diabetes Risk Score, trained on our data, achieving auROC of 0.74 (0.7-0.77) and 0.63 (0.59-0.67), respectively.

scholarly journals A non-exercise method to determine cardiorespiratory fitness identifies females predicted to be at ‘high risk’ of type 2 diabetes

2016 ◽  
Vol 14 (1) ◽  
pp. 47-54 ◽  
Author(s):  
Benjamin J Gray ◽  
Jeffrey W Stephens ◽  
Daniel Turner ◽  
Michael Thomas ◽  
Sally P Williams ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
High Risk ◽  
Risk Score ◽  
Exercise Testing ◽  
Diabetes Risk ◽  
Testing Method ◽  
Metabolic Equivalents

This study examined the relationship between cardiorespiratory fitness determined by a non-exercise testing method for estimating fitness and predicted risk of developing type 2 diabetes mellitus using five risk assessments/questionnaires (Leicester Diabetes Risk Score, QDiabetes, Cambridge Risk Score, Finnish Diabetes Risk Score and American Diabetes Association Diabetes Risk Test). Retrospective analysis was performed on 330 female individuals with no prior diagnosis of cardiovascular disease or type 2 diabetes mellitus who participated in the Prosiect Sir Gâr workplace initiative in Carmarthenshire, South Wales. Non-exercise testing method for estimating fitness (expressed as metabolic equivalents) was calculated using a validated algorithm, and females were grouped accordingly into fitness quintiles <6.8 metabolic equivalents (Quintile 1), 6.8–7.6 metabolic equivalents (Quintile 2), 7.6–8.6 metabolic equivalents (Quintile 3), 8.6–9.5 metabolic equivalents (Quintile 4), >9.5 metabolic equivalents (Quintile 5). Body mass index, waist circumference, and HbA1c all decreased between increasing non-exercise testing method for estimating fitness quintiles ( p < 0.05), as did risk prediction scores in each of the five assessments/questionnaires ( p < 0.05). The proportion of females in Quintile 1 predicted at ‘high risk’ was between 20.9% and 81.4%, depending on diabetes risk assessment used, compared to none of the females in Quintile 5. A calculated non-exercise testing method for estimating fitness <6.8 metabolic equivalents could help to identify females at ‘high risk’ of developing type 2 diabetes mellitus as predicted using five risk assessments/questionnaires.

scholarly journals Assessing the risk of developing type 2 diabetes mellitus among medical students in Chennai using Indian diabetes risk score

2017 ◽  
Vol 4 (7) ◽  
pp. 2366
Author(s):  
Gopalakrishnan S. ◽  
Rama R. ◽  
Muthulakshmi M.
Keyword(s):  
Physical Activity ◽  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Medical Students ◽  
Risk Score ◽  
Family History Of Diabetes ◽  
History Of ◽  
Diabetes Risk Score

Background: Prevalence of type 2 diabetes mellitus [T2DM] is becoming alarmingly high among younger age groups impacting on their physical, mental, social and academic wellbeing and therefore warrants early detection and prevention. The Indian diabetes risk score [IDRS] is an efficient screening tool to detect the high risk individuals at an early stage. Objective of this study is to assess the level of risk of developing T2DM among medical students using the IDRS.Methods: This cross sectional study was done using the MDRF-IDR Score to identify the ‘at risk’ medical students. Simple random sampling was used and data collected from among the 251 willing students. Their risk score was calculated using a structured questionnaire. Data was analysed using SPSS Ver.15 software.Results: This study shows that about 57.4% are moderately at risk and 2% are at high risk for developing diabetes mellitus. About 86.1% medical students belonged to nuclear family, 42.6% had family history of diabetes mellitus, 76.5% carried out moderate physical activity and 50.2% were overweight / obese. Family history of diabetes, lack of physical activity and overweight / obesity were found to be potential risk factors for developing diabetes mellitus (p<0.0001).Conclusions: This study reveals that in the existing urban lifestyle, adolescents and youths are highly vulnerable to diabetes mellitus. Primordial and primary prevention are the most effective preventive measure and therefore, appropriate and stringent lifestyle modifications need to be implemented in order to minimize the risk of developing the disease later in life.

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