Musculoskeletal Ultrasound Detection and Mechanistic Evaluations of a Type 2 Diabetes (T2D)- and Prediabetes (PreD)-Related Skeletal Muscle Abnormality. This research interest originated during my fellowship in MSK radiology in 2009. I hypothesized that increased skeletal muscle echo intensity (hyperechoic) observed in PreD and early T2D could serve as a simple, noninvasive, nonionizing, and inexpensive imaging biomarker for early detection of insulin resistance, decreased lean muscle mass, and muscle dysfunction. In 2017, as the PI, I initiated a retrospective study to evaluate this skeletal muscle US abnormality. The resulting abstract was accepted at the RSNA Annual Meeting in 2018 and chosen for its media press release, leading to international media coverage. This included a formal interview aired on local NBC news in December 2018. Furthermore, a resulting publication in the Journal of Ultrasound in Medicine became the most downloaded article in 2018-2019, reinforcing the scientific and public interest in this skeletal muscle abnormality. Following this success, I began another study as PI to objectively evaluate this sonographic abnormality using a quantitative US method. This resulted in a publication in BMC Endocrine Disorders. Ongoing studies include a recent 1-year NIH R56 DK133298 grant, entitled "Early Type 2 Diabetes Detection with AI-Analysis of Skeletal Muscle Ultrasound and Mechanistic Evaluations" and a recent publication: Khan S, Klochko CL, Cooper S, Franz B, Wolf L, Alessio A, Soliman SB. Skeletal Muscle Ultrasound Radiomics and Machine Learning for the Earlier Detection of Type 2 Diabetes Mellitus. J Med Ultrasound. 26 June 2024. doi: 10.4103/jmu.jmu_12_24. Additional preliminary studies in both human subjects and murine models with histologic analyses reinforce our hypothesis that this abnormality signifies developing insulin resistance, lower muscle mass, and muscle dysfunction, as a harbinger of T2D. Expanding our understanding of the factors in skeletal muscle that underlie elevated muscle echo intensity could help optimize treatments to prevent or treat this abnormality, leading to better patient outcomes.
Globally, an astounding 232 million people, or 50% of those with T2D, and 438 million people, or 81% of those with PreD, are unaware and undiagnosed. In the United States alone, a staggering 8.5 million individuals with T2D and a vast 88.2 million individuals, or 90% of those with PreD, are undiagnosed. This affects nearly one-third of the U.S. population with a disproportionate impact on underserved, underrepresented, and lower socioeconomic communities, which make up 79% of the T2D population. Furthermore, when T2D is finally detected, approximately half of the patients already suffer from one or more irreversible complications, contributing to an economic burden of at least $413 billion in the U.S. and $966 billion globally. Early noninvasive detection using an AI/ML based method, leveraging skeletal muscle as a biomarker, could halt or delay disease progression, reduce irreversible complications, and help mitigate this substantial economic burden.