Key result
High intramuscular fat on AI ultrasound is linked to ~501% higher odds of diabetic nephropathy.
Why the study?
Intramuscular fat is recognized as a marker of ectopic adiposity and adverse cardiometabolic outcomes, but its association with diabetes-related complications and metabolic risk factors needed evaluation.
Is intramuscular fat assessed by AI-assisted ultrasound associated with diabetic nephropathy and cardiometabolic risk factors in patients with diabetes mellitus?
Cross-Sectional (n=120)
No
Is intramuscular fat assessed by AI-assisted ultrasound associated with diabetic nephropathy and cardiometabolic risk factors in patients with diabetes mellitus?
Odds Ratio: 6.01 (95% CI 1.99–18.14)
Absolute Event Rate: 44% vs 14.7%
p-value: p=<0.01
AI-assisted ultrasound quantification of intramuscular fat identifies patients with diabetes at higher risk for diabetic nephropathy and adverse metabolic profiles.
AI-assisted IMF measurement may flag higher-risk DM patients; leaves open causal links to nephropathy and need for longitudinal validation.
Aims Intramuscular fat (IMF) is increasingly recognized as a marker of ectopic adiposity and adverse cardiometabolic outcomes. Artificial intelligence (AI)‐assisted ultrasound of the rectus femoris (RF) offers a non‐invasive approach for quantifying IMF. This study evaluated the association of IMF with diabetes‐related complications (particularly diabetic nephropathy) and metabolic risk factors in patients with diabetes mellitus (DM). Materials and Methods In this cross‐sectional study, outpatients from a tertiary Endocrinology and Nutrition Department underwent anthropometric assessment, bioimpedance analysis, and muscular ultrasound. Ultrasound images were analysed using the PIIXMED AI‐system (DAWAKO MedTech; Valencia, Spain) to quantify muscle (Mi) and fat (FATi) percentages (the latter indicating the IMF). Associations between IMF, clinical characteristics, metabolic biomarkers, and vascular complications were examined. Results A total of 120 patients were included (57.5% men), with a mean age of 70.8 ± 10.3 years and diabetes duration of 13.8 ± 9.6 years. Most participants had type 2 DM (79.2%), with suboptimal glycaemic control (HbA1c 8.1% ± 1.5%). Individuals in the highest FATi quartile (> 43%) showed higher prevalence of microvascular complications, particularly diabetic nephropathy (44.0 vs. 14.7%, p = 0.001), and lower estimated glomerular filtration rate (63.6 ± 21.3 vs. 72.8 ± 20.1 mL/min/1.73m 2 , p = 0.045). Multivariate analysis showed that FATi remained independently associated with nephropathy (OR 6.01, 95% CI 1.99–18.14; p < 0.01). ROC analysis identified a FATi threshold of 43.5% with modest discriminative ability (AUC = 0.668). Conclusions AI‐assisted ultrasound‐derived FATi is associated with adverse metabolic profiles and diabetic nephropathy. Assessment of IMF may provide an accessible biomarker related to microvascular complications. Longitudinal and multicentre studies are needed to determine the role of IMF in diabetes‐related complications.
No takes yet. Share an insight, caveat, or question.
Pérez‐López et al. (2026) conducted a cross-sectional in diabetes mellitus (n=120). High intramuscular fat (FATi > 43%) vs. Lower intramuscular fat (FATi ≤ 43%) was evaluated on diabetic nephropathy (OR 6.01, 95% CI 1.99-18.14, p=<0.01). Higher intramuscular fat (>43%) assessed by AI-assisted ultrasound was independently associated with a higher prevalence of diabetic nephropathy (OR 6.01; 95% CI 1.99-18.14; p<0.01).
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: