Abstract
Obesity is one of the main drivers of type 2 diabetes, but it is not uniformly associated with the disease. The location of fat accumulation is critical for metabolic health. Specific patterns of body fat distribution, such as visceral fat, are closely related to insulin resistance. There might be further, hitherto unknown, features of body fat distribution that could additionally contribute to the disease. We used machine learning with dense convolutional neural networks to detect diabetes-related variables from 2371 T1-weighted whole-body MRI data sets. MRI was performed in participants undergoing metabolic screening with oral glucose tolerance tests. Models were trained for sex, age, BMI, insulin sensitivity, HbA1c, and prediabetes or incident diabetes. The results were compared with those of conventional models. The area under the receiver operating characteristic curve was 87% for the type 2 diabetes discrimination and 68% for prediabetes, both superior to conventional models. Mean absolute regression errors were comparable to those of conventional models. Heatmaps showed that lower visceral abdominal regions were critical in diabetes classification. Subphenotyping revealed a group with high future diabetes and microalbuminuria risk.Our results show that diabetes is detectable from whole-body MRI without additional data. Our technique of heatmap visualization identifies plausible anatomical regions and highlights the leading role of fat accumulation in the lower abdomen in diabetes pathogenesis.
Authors
Benedikt Dietz, Jürgen Machann, Vaibhav Agrawal, Martin Heni, Patrick Schwab, Julia Dienes, Steffen Reichert, Andreas L. Birkenfeld, Hans-Ulrich Häring, Fritz Schick, Norbert Stefan, Andreas Fritsche, Hubert Preissl, Bernhard Schölkopf, Stefan Bauer, Robert Wagner
This file is in Adobe Acrobat (PDF) format. If you have not installed and configured the Adobe Acrobat Reader on your system.
Having trouble reading a PDF?
PDFs are designed to be printed out and read, but if you prefer to read them online, you may find it easier if you increase the view size to 125%.
Having trouble saving a PDF?
Many versions of the free Acrobat Reader do not allow Save. You must instead save the PDF from the JCI Online page you downloaded it from. PC users: Right-click on the Download link and choose the option that says something like "Save Link As...". Mac users should hold the mouse button down on the link to get these same options.
Having trouble printing a PDF?
- Try printing one page at a time or to a newer printer.
- Try saving the file to disk before printing rather than opening it "on the fly." This requires that you configure your browser to "Save" rather than "Launch Application" for the file type "application/pdf", and can usually be done in the "Helper Applications" options.
- Make sure you are using the latest version of Adobe's Acrobat Reader.
