Beyond BMI: Altered Body Composition in Rheumatoid Arthritis Save
Dr. Mrinalini Dey discusses abstract OP0348, presented at the 2026 EULAR meeting in London.
Transcription
Hello, my name is Minolani Day. I'm a fellow in rheumatology and internal medicine working at King's College London in the UK. And I'm delighted to be reporting from EULAR 2026 for RheumNow from here in London. And today I would like to highlight one of the imaging abstracts being presented at EULAR 2026. And this comes from the Mayo Clinic. It's OP0348. So this work used advanced CT-based body composition analysis to better understand how rheumatoid arthritis alters fat and muscle biology.
So we know from many prior studies that body composition does change in rheumatoid arthritis and most of these studies have relied on the use of BMI or DEXA scans for example. But importantly, these approaches can miss important differences in where fat is distributed and how healthy muscle tissue actually is.
So here the investigators analyzed abdominal CT scans from almost 900 individuals using a validated deep learning segmentation approach to quantify subcutaneous fat, visceral fat, intramuscular fat, muscle area and muscle radiodensity. The key finding from their study was that patients with rheumatoid arthritis had a very distinct body composition profile compared with matched non-RA controls. So they had significantly more subcutaneous fat, more intermuscular adipose tissue and essentially fat infiltration between muscle groups, and lower muscle radiodensity which is a marker of reduced muscle quality or myosteatosis.
Now what is particularly interesting here is that overall skeletal muscle area and visceral fat were not significantly different. So rheumatoid arthritis seems to be associated with a qualitative remodeling of muscle and fat tissue that conventional metrics like BMI, which we are so used to measuring, may completely actually fail to capture. And that matters because clinically intermuscular fat accumulation and myosteatosis are increasingly associated with frailty, disability, insulin resistance and cardiovascular risk. So this does have important implications for our rheumatoid arthritis patients and for the development of comorbidities which of course we know that they are already predisposed to. It may help explain why some patients with rheumatoid arthritis appear metabolically unhealthy despite having relatively normal body weight.
Another important aspect is the imaging methodology itself. So the use of deep learning CT segmentation allowed detailed phenotyping in this case from routine clinical imaging, suggesting that opportunistic CT analysis could eventually become a scalable way to identify high-risk metabolic and musculoskeletal phenotypes in people with rheumatoid arthritis.
So overall, this study shifts that conversation away from simply looking at just obesity or sarcopenia, but towards understanding how chronic inflammation may fundamentally alter tissue composition and muscle quality in ways that are very clinically meaningful. So if you want to know more about this particular work, it is OP0348 in the imaging abstract session. And if you'd like to know more about everything going on here at EULAR 2026, do be sure to follow us along on RheumNow.
So we know from many prior studies that body composition does change in rheumatoid arthritis and most of these studies have relied on the use of BMI or DEXA scans for example. But importantly, these approaches can miss important differences in where fat is distributed and how healthy muscle tissue actually is.
So here the investigators analyzed abdominal CT scans from almost 900 individuals using a validated deep learning segmentation approach to quantify subcutaneous fat, visceral fat, intramuscular fat, muscle area and muscle radiodensity. The key finding from their study was that patients with rheumatoid arthritis had a very distinct body composition profile compared with matched non-RA controls. So they had significantly more subcutaneous fat, more intermuscular adipose tissue and essentially fat infiltration between muscle groups, and lower muscle radiodensity which is a marker of reduced muscle quality or myosteatosis.
Now what is particularly interesting here is that overall skeletal muscle area and visceral fat were not significantly different. So rheumatoid arthritis seems to be associated with a qualitative remodeling of muscle and fat tissue that conventional metrics like BMI, which we are so used to measuring, may completely actually fail to capture. And that matters because clinically intermuscular fat accumulation and myosteatosis are increasingly associated with frailty, disability, insulin resistance and cardiovascular risk. So this does have important implications for our rheumatoid arthritis patients and for the development of comorbidities which of course we know that they are already predisposed to. It may help explain why some patients with rheumatoid arthritis appear metabolically unhealthy despite having relatively normal body weight.
Another important aspect is the imaging methodology itself. So the use of deep learning CT segmentation allowed detailed phenotyping in this case from routine clinical imaging, suggesting that opportunistic CT analysis could eventually become a scalable way to identify high-risk metabolic and musculoskeletal phenotypes in people with rheumatoid arthritis.
So overall, this study shifts that conversation away from simply looking at just obesity or sarcopenia, but towards understanding how chronic inflammation may fundamentally alter tissue composition and muscle quality in ways that are very clinically meaningful. So if you want to know more about this particular work, it is OP0348 in the imaging abstract session. And if you'd like to know more about everything going on here at EULAR 2026, do be sure to follow us along on RheumNow.
If you are a health practitioner, you may Login/Register to comment.
Due to the nature of these comment forums, only health practitioners are allowed to comment at this time.