A shoulder muscle that appears unusually bright on ultrasound may be a warning sign of diabetes, according to a study presented at RSNA 2018.
More than 10 years ago, musculoskeletal radiologist Steven B. Soliman, DO, from Henry Ford Hospital in Detroit, began noticing a pattern when images of the deltoid muscle, the largest muscle of the shoulder, appeared bright on ultrasound. “Every time we would ask one of these patients if they were diabetic, they would say ‘yes’ or they would tell us they were borderline and not taking any medications,” Soliman said.
The observations prompted Soliman and colleagues at Henry Ford to conduct a study to see whether the brightness, or echogenicity, of the shoulder muscle could be predictive of diabetes. The results revealed that by using the echogenicity of the muscle, radiologists were able to predict type 2 diabetes, the most common type of diabetes, in almost nine out of 10 patients.
Brightness on ultrasound also was an accurate predictor of prediabetes, a condition of abnormally high blood sugar that generally progresses to diabetes without changes in lifestyle.
The researchers believe the findings could allow for earlier interventions. “If we observe this in patients with prediabetes and diabetes, we can get them to exercise, make diet modifications, and lose weight,” Soliman said. “If these interventions happen early enough, the patients may be able to avoid going on medications and dealing with all the complications that go with the disease.”
For the study, Soliman and colleagues compiled 137 shoulder ultrasounds from patients with type 2 diabetes, including 13 with prediabetes. The researchers also obtained 49 ultrasounds from obese patients without diabetes. The researchers showed the ultrasounds to two musculoskeletal radiologists who were unaware whether the images came from patients with or without diabetes. The radiologists were asked to classify the patients, based on the brightness of their shoulder muscle, into one of three categories: normal, suspected diabetes, and definite diabetes. A third musculoskeletal radiologist acted as an arbitrator in the cases where the other two radiologists disagreed.
The results showed that a consensus diagnosis of “definite diabetes” by the radiologists was a powerful predictor of diabetic status. Using the shoulder ultrasounds, the radiologists correctly predicted diabetes in 70 of 79 patients (89%).
“We weren’t surprised that we had positive results, because the shoulder muscle on patients with diabetes looked so bright on ultrasound, but we were surprised at the level of accuracy,” Soliman said.
A hyperechoic, or unusually bright-looking, deltoid muscle was also a strong predictor of prediabetes. The musculoskeletal radiologists assigned all 13 prediabetic ultrasounds to either the “suspected diabetes” or “definite diabetes” categories.
“A lot of the patients weren’t even aware that they were diabetic or prediabetic,” said Soliman, who noted that this lack of awareness is a major problem in the United States.
According to the Centers for Disease Control and Prevention (CDC), nearly one in four Americans with diabetes—about 7.2 million people—are unaware they have the disease and are left undiagnosed.
“Also, the CDC states that prediabetes affects an astonishing 84.1 million adults, or nearly 34% of the adult US population, and an overwhelming 90% of these people are completely unaware of their prediabetic status and are at a high risk of developing type 2 diabetes,” Soliman said.
The reasons for the brighter-appearing shoulder muscle on ultrasound among patients with diabetes are not completely understood, according to Soliman, but the researchers suspect it is due to low levels of glycogen in the muscle, a key source of energy for the body that is stored primarily in the liver and muscles. A study of muscle biopsies in patients with diabetes found that muscle glycogen levels are decreased up to 65%. Prior research has also shown that the muscles of athletes appear brighter on ultrasound after exercise, when their glycogen stores are depleted.
“It could be that this appearance in people with diabetes and prediabetes is related to the known problems with glycogen synthesis in their muscles because of their insulin abnormalities,” Soliman said.
If they see a bright shoulder muscle on ultrasound, radiologists at Henry Ford now put notes in their reports indicating that this observation has been linked to diabetes. The researchers plan to continue studying the connection between shoulder muscle echogenicity and diabetes with an eye toward quantifying the phenomenon and seeing whether it is reversible.
MRI brain scans perform better than common clinical tests at predicting which people will go on to develop Alzheimer’s disease, according to a study presented at RSNA 2018. Alzheimer’s disease is a progressive, irreversible brain disorder that destroys memory and thinking skills. The disease affects 5.5 million Americans, according to the National Institutes of Health.
“Alzheimer’s disease is the most common cause of dementia in the world and is expected to increase globally, and especially in the United States, as the population gets older,” said the study’s lead author Cyrus A. Raji, MD, PhD, an assistant professor of radiology at the Mallinckrodt Institute of Radiology at Washington University School of Medicine in St. Louis. “As we develop new drug therapies and study them in trials, we need to identify individuals who will benefit from these drugs earlier in the course of the disease.”
Common predictive models, such as standardized questionnaires used to measure cognition and tests for the associated APOE4 gene, have limitations and—with accuracy rates of about 70% to 71%—fail to identify many people who go on to develop the disease. MRI exams of the brain using diffusion tensor imaging (DTI) are a promising option for analysis of dementia risk; these exams assess the condition of the brain’s white matter.
“With DTI, you look at the movement of water molecules along white matter tracts, the telephone cables of the brain,” Raji said. “When these tracts are not well connected, cognitive problems can result.”
DTI provides different metrics of white matter integrity, including fractional anisotropy (FA), a measure of how well water molecules move along white matter tracts. A higher FA value indicates that water is moving in a more orderly fashion along the tracts, while a lower value means that the tracts are likely damaged.
For the study, Raji and colleagues set out to quantify differences in DTI in people who decline from normal cognition or mild cognitive impairment to Alzheimer’s dementia, compared with controls who do not develop dementia. They performed brain DTI exams on 61 people drawn from the Alzheimer’s Disease Neuroimaging Initiative—a major, multisite study focusing on the progression of the disease.
About one-half of the patients went on to develop Alzheimer’s disease, and DTI identified quantifiable differences in the brains of those patients. People who developed the disease had lower FA compared with those who didn’t, suggesting white matter damage. They also had statistically significant reductions in certain frontal white matter tracts.
“DTI performed very well compared to other clinical measures,” Raji said. “Using FA values and other associated global metrics of white matter integrity, we were able to achieve 89% accuracy in predicting who would go on to develop Alzheimer’s disease. The Mini-Mental State Examination and APOE4 gene testing have accuracy rates of about 70% to 71%.”
The researchers conducted a more detailed analysis of the white matter tracts in about 40 of the study participants. Among those patients, the technique achieved 95% accuracy, according to Raji.
While more work is needed before the approach is ready for routine clinical use, the results point to a future role for DTI in the diagnostic workup of people at risk for Alzheimer’s disease. Many people already receive MRI as part of their care, so DTI could add significant value to the exam without substantially increasing the costs, Raji said.
Perhaps most importantly, MRI measures of white matter integrity could speed interventions that slow the course of the disease or even delay its onset.
“Research shows that Alzheimer’s disease risk can be reduced by addressing modifiable risk factors like obesity and diabetes,” Raji said. “With early detection, we can enact lifestyle interventions and enlist volunteers into drug trials earlier.”
— Source: Press materials distributed at RSNA 2018