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Ronald Klein

Researcher at University of Wisconsin-Madison

Publications -  1306
Citations -  163459

Ronald Klein is an academic researcher from University of Wisconsin-Madison. The author has contributed to research in topics: Population & Diabetes mellitus. The author has an hindex of 194, co-authored 1305 publications receiving 149140 citations. Previous affiliations of Ronald Klein include Los Angeles Biomedical Research Institute & Wake Forest University.

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Modeling Chronic Glycemic Exposure Variables as Correlates and Predictors of Microvascular Complications of Diabetes Response to Orchard et al.

TL;DR: In this article, the authors investigated whether composite measures of chronic glycemia correlate or predict complications better than individual components, while Orchard et al. reported evidence against the hypothesis, while we reported evidence for the hypothesis.
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Low Birthweight Is Associated With Narrower Arterioles in Adults

TL;DR: Evidence is provided that structural alterations in the arteriolar microcirculation may be a potential mechanism linking restricted fetal growth with subsequent risk of hypertension, and this association was also present in persons without hypertension or diabetes.
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Retinal microvascular changes and MRI signs of cerebral atrophy in healthy, middle-aged people.

TL;DR: In healthy, middle-aged people, retinopathy is independently associated with sulcal and ventricular enlargement on MRI, compatible with the hypothesis that microvascular characteristics may influence the development of cerebral atrophic changes.
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Lower-than-Expected Prevalence and Severity of Retinopathy in an Incident Cohort followed during the First 4–14 Years of Type 1 Diabetes: The Wisconsin Diabetes Registry Study

TL;DR: Improvements in diabetes care leading to better glycemic control may have contributed to the much lower prevalence and less severe retinopathy observed than expected on the basis of a previous report from the same region of Wisconsin.
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Signal quality assessment of retinal optical coherence tomography images.

TL;DR: In this article, an intensity histogram decomposition model was proposed to separate the foreground and background information of OCT images and to calculate the maximum tissue contrast index (mTCI), which was compared with the manufacturer signal index (MSI) provided by the respective devices and to the subjective grading scores (SGS).