Prediction of incident diabetes mellitus in middle-aged adults: the Framingham Offspring Study.

By Peter W. F. Wilson, James B. Meigs, Lisa Sullivan, Caroline S. Fox, D. Nathan, Ralph B. D’Agostino. Archives of Internal Medicine, 2007

1,002 citations recorded by the source corpus.

Subfields: Endocrinology, Diabetes and Metabolism, Epidemiology, Physiology

View Paper: https://www.semanticscholar.org/paper/ad3e12262a8a2deffb4dcd7783a0d3d7c761b227

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Endocrinology, Diabetes and Metabolism

1. S. Grundy
2. G. Bakris
3. Å. Lernmark
4. D. Nathan
5. M. S. Kirkman
6. R. Eckel
7. P. Zimmet
8. V. Basevi

In Epidemiology

1. M. S. Kirkman
2. S. Grundy
3. R. Eckel
4. P. Zimmet

In Physiology

1. D. Nathan
2. S. Grundy
3. R. Eckel
4. P. Zimmet
5. I. Neeland

Citations in the corpus

• Dysfunctional Adiposity and the Risk of Prediabetes and Type 2 Diabetes in Obese Adults (2012, methodology): “85), outperforming a clinical model developed previously in a white, nonobese population.(32) Further research is needed to determine whether assessment of adipose tissue distribution and function using imaging tools, circulating biomarkers, or both can improve clinical risk prediction in obese indi”
• The metabolic syndrome. (2003, positive): “From this study group, a simple clinical model was developed that was able to identify subjects with an elevated risk for diabetes [37].”
• Guidelines and recommendations for laboratory analysis in the diagnosis and management of diabetes mellitus. (2002, background): “It is these variables that are the focus of clinical interventions, not plasma insulin or proinsulin concentrations (366,367).”
• Metabolite Profiles and the Risk of Developing Diabetes (2011, neutral): “Current clinical and laboratory predictors such as body mass index or fasting glucose can be helpful in gauging diabetes ris”
• Predicting Current Glycated Hemoglobin Values in Adults: Development of an Algorithm From the Electronic Health Record (2018, neutral): “Most of these tools are not practical in the setting of the electronic health record (EHR) because they include predictor variables not readily available in structured formats [1-20].”
• Guidelines and Recommendations for Laboratory Analysis in the Diagnosis and Management of Diabetes Mellitus (2002, background): “It is these variables that are the focus of clinical interventions, not plasma insulin or proinsulin concentrations (366, 367).”