scholarly journals An Accurate Risk Score Based on Anthropometric, Dietary, and Lifestyle Factors to Predict the Development of Type 2 Diabetes

Diabetes Care ◽  
2007 ◽  
Vol 30 (3) ◽  
pp. 510-515 ◽  
Author(s):  
M. B. Schulze ◽  
K. Hoffmann ◽  
H. Boeing ◽  
J. Linseisen ◽  
S. Rohrmann ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Risk Score ◽  
Lifestyle Factors

scholarly journals An Accurate Risk Score Based on Anthropometric, Dietary, and Lifestyle Factors to Predict the Development of Type 2 Diabetes: Response to Schulze et al.

Diabetes Care ◽  
2007 ◽  
Vol 30 (8) ◽  
pp. e87-e87 ◽  
Author(s):  
P. E.H. Schwarz ◽  
J. Li ◽  
H. Wegner ◽  
S. R. Bornstein ◽  
J. Lindstrom ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Risk Score ◽  
Lifestyle Factors

scholarly journals An Accurate Risk Score Based on Anthropometric, Dietary, and Lifestyle Factors to Predict the Development of Type 2 Diabetes: Response to Schwarz et al.

Diabetes Care ◽  
2007 ◽  
Vol 30 (8) ◽  
pp. e88-e88 ◽  
Author(s):  
M. B. Schulze ◽  
H.-U. Haring ◽  
A. F.H. Pfeiffer ◽  
H.-G. Joost
Keyword(s):  
Type 2 Diabetes ◽  
Risk Score ◽  
Lifestyle Factors

1645-P: Polygenic Risk Score for Prediction of Complications in Men and Women with Type 2 Diabetes

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 1645-P
Author(s):  
JOHANNE TREMBLAY ◽  
REDHA ATTAOUA ◽  
MOUNSIF HALOUI ◽  
RAMZAN TAHIR ◽  
CAROLE LONG ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Risk Score ◽  
Polygenic Risk Score ◽  
Men And Women ◽  
Polygenic Risk ◽  
Prediction Of Complications

304-OR: Polygenic Risk Score Predicts Type 2 Diabetes Susceptibility in a Diverse Consumer Genetic Database

Diabetes ◽  
2019 ◽  
Vol 68 (Supplement 1) ◽  
pp. 304-OR
Author(s):  
MICHAEL L. MULTHAUP ◽  
RYOSUKE KITA ◽  
NICHOLAS ERIKSSON ◽  
STELLA ASLIBEKYAN ◽  
JANIE SHELTON ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Risk Score ◽  
Polygenic Risk Score ◽  
Diabetes Susceptibility ◽  
Genetic Database ◽  
Polygenic Risk

Menstrual Cycle Characteristics Across the Reproductive Lifespan, Lifestyle Factors and Risk of Type 2 Diabetes

2020 ◽  
Author(s):  
Yi-Xin Wang ◽  
Zhilei Shan ◽  
Mariel Arvizu ◽  
An Pan ◽  
JoAnn E. Manson ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Menstrual Cycle ◽  
Lifestyle Factors

scholarly journals A novel drug response score more accurately predicts renoprotective drug effects than existing renal risk scores

2021 ◽  
Vol 12 ◽  
pp. 204201882097419
Author(s):  
Nienke M. A. Idzerda ◽  
Sok Cin Tye ◽  
Dick de Zeeuw ◽  
Hiddo J. L. Heerspink
Keyword(s):  
Type 2 Diabetes ◽  
Kidney Disease ◽  
Serum Creatinine ◽  
Risk Score ◽  
Drug Response ◽  
Drug Effects ◽  
Risk Scores ◽  
Renal Outcome ◽  
Response Score

Background: Risk factor-based equations are used to predict risk of kidney disease progression in patients with type 2 diabetes order to guide treatment decisions. It is, however, unknown whether these models can also be used to predict the effects of drugs on clinical outcomes. Methods: The previously developed Parameter Response Efficacy (PRE) score, which integrates multiple short-term drug effects, was first compared with the existing risk scores, Kidney Failure Risk Equation (KFRE) and The Action in Diabetes and Vascular Disease: Preterax and Diamicron Modified Release Controlled Evaluation (ADVANCE) renal risk score, in its performance to predict end-stage renal disease (ESRD; KFRE) and doubling of serum creatinine or ESRD (ADVANCE). Second, changes in the risk scores were compared after 6 months’ treatment to predict the long-term effects of losartan on these renal outcomes in patients with type 2 diabetes and chronic kidney disease. Results: The KFRE, ADVANCE and PRE scores showed similarly good performance in predicting renal risk. However, for prediction of the effect of losartan, the KFRE risk score predicted a relative risk change in the occurrence of ESRD of 3.1% [95% confidence interval (CI) −5 to 12], whereas the observed risk change was −28.8% (95% CI −42.0 to −11.5). For the composite endpoint of doubling of serum creatinine or ESRD, the ADVANCE score predicted a risk change of −12.4% (95% CI −17 to −7), which underestimated the observed risk change −21.8% (95% CI −34 to −6). The PRE score predicted renal risk changes that were close to the observed risk changes with losartan treatment [−24.0% (95% CI −30 to −17) and −22.6% (95% CI −23 to −16) for ESRD and the composite renal outcome, respectively]. Conclusion: A drug response score such as the PRE score may assist in improving clinical decision making and implement precision medicine strategies.

scholarly journals Development of Risk Score for Predicting 3-Year Incidence of Type 2 Diabetes: Japan Epidemiology Collaboration on Occupational Health Study

PLoS ONE ◽  
2015 ◽  
Vol 10 (11) ◽  
pp. e0142779 ◽  
Author(s):  
Akiko Nanri ◽  
Tohru Nakagawa ◽  
Keisuke Kuwahara ◽  
Shuichiro Yamamoto ◽  
Toru Honda ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Occupational Health ◽  
Risk Score ◽  
Health Study

scholarly journals Validity of the Finnish Diabetes Risk Score for Detecting Undiagnosed Type 2 Diabetes among General Medical Outpatients in Botswana

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Bernard Omech ◽  
Julius Chacha Mwita ◽  
Jose-Gaby Tshikuka ◽  
Billy Tsima ◽  
Oathokwa Nkomazna ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Risk Score ◽  
Predictive Value ◽  
Total Population ◽  
Diabetes Risk ◽  
Undiagnosed Diabetes ◽  
Haemoglobin A1c ◽  
Diabetes Risk Score ◽  
Medical Outpatients

This was a cross-sectional study designed to assess the validity of the Finnish Diabetes Risk Score for detecting undiagnosed type 2 diabetes among general medical outpatients in Botswana. Participants aged ≥20 years without previously diagnosed diabetes were screened by (1) an 8-item Finnish diabetes risk assessment questionnaire and (2) Haemoglobin A1c test. Data from 291 participants were analyzed (74.2% were females). The mean age of the participants was 50.1 (SD = ±11) years, and the prevalence of undiagnosed diabetes was 42 (14.4%) with no significant differences between the gender (20% versus 12.5%,P=0.26). The area under curve for detecting undiagnosed diabetes was 0.63 (95% CI 0.55–0.72) for the total population, 0.65 (95% CI: 0.56–0.75) for women, and 0.67 (95% CI: 0.52–0.83) for men. The optimal cut-off point for detecting undiagnosed diabetes was 17 (sensitivity = 48% and specificity = 73%) for the total population, 17 (sensitivity = 56% and specificity = 66%) for females, and 13 (sensitivity = 53% and specificity = 77%) for males. The positive predictive value and negative predictive value were 20% and 89.5%, respectively. The findings indicate that the Finnish questionnaire was only modestly effective in predicting undiagnosed diabetes among outpatients in Botswana.

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