Two new studies published this week highlight the exciting potential of medical imaging scans to guide preventive health by analyzing the composition of tissues like muscle and fat. The research suggests that imaging can predict a person’s risk of serious disease years before symptoms occur.
Previous research has shown that the composition of various types of body tissue can serve as biomarkers for future health problems, particularly cardiovascular disease and metabolic issues like diabetes.
- Advances in AI analysis are driving much of the new understanding, as increasingly powerful deep learning algorithms are emerging that can analyze massive quantities of imaging data and compare tissue composition to databases of normal scans.
The new studies – both published in Radiology – test this concept on a large scale. In the first, researchers in Germany reviewed MRI data from over 66k people who got whole-body MRI scans as part of large population health studies (UK Biobank and German National Cohort, or NAKO).
- They used deep learning algorithms to create z-scores of tissue composition metrics and correlated those measurements to the risk of developing conditions like diabetes and cardiac events, as well as all-cause mortality.
Researchers found…
- High visceral adipose tissue indicated higher risk of incident diabetes (HR = 2.26).
- High intramuscular adipose tissue was connected to major adverse cardiovascular events (HR = 1.54).
- Low skeletal muscle was linked to all-cause mortality (HR = 1.44).
In the second study, researchers focused on 11.3k participants in the NAKO study who got whole-body 3T MRI scans.
- Researchers used software to analyze two tissue composition metrics, paraspinal intermuscular adipose tissue (IMAT) and lean muscle mass (LMM), which only recently have been connected to metabolic dysfunction.
The authors found…
- Increased IMAT was associated with higher risk of hypertension and atherogenic dyslipidemia, a lipid imbalance associated with metabolic dysfunction and diabetes risk (HR = 1.67 and 1.82, respectively).
- Higher LMM was a marker for better health in men, and was linked to lower odds of hypertension and atherogenic dyslipidemia (HR = 0.34 and 0.49, respectively).
The Takeaway
The new studies build on previous research to show how the combination of imaging-derived biomarkers and AI-based analysis can pinpoint currently healthy people who might be at higher risk of future disease. The implications are exciting for anyone who believes radiology can play a greater role in guiding population health.
