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TRUVACE RECORD VERSION
record: TRV-2026-0900
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-08-26T06:07:08.692898Z
status: published
lens: g_space
sector: health
headline: Intramuscular Fat Assessed by AI-Assisted Muscle Ultrasound: Association With Cardiometabolic Risk Factors and Diabetic Nephropathy in Diabetes Mellitus
dek: 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 tertia…
gain_title: AI-assisted ultrasound of the rectus femoris quantified intramuscular fat percentage (FATi), which was independently associated with diabetic nephropathy and adverse metabolic profiles in patients with diabetes.
problem_title: (none)
trace_subject: (none)
gain_reading: AI-assisted ultrasound of the rectus femoris quantified intramuscular fat percentage (FATi), which was independently associated with diabetic nephropathy and adverse metabolic profiles in patients with diabetes.
gain_evidence: AI-assisted ultrasound-derived FATi is associated with adverse metabolic profiles and diabetic nephropathy. | FATi remained independently associated with nephropathy (OR 6.01, 95% CI 1.99-18.14
problem_reading: (none)
problem_evidence: (none)
quick_read: In a cross-sectional study of 120 diabetes outpatients at a tertiary Endocrinology and Nutrition Department, researchers used the PIIXMED AI-system to analyze rectus femoris ultrasound images and quantify intramuscular fat percentage (FATi). By publication date 2026-08-24, they reported that higher FATi was associated with adverse metabolic profiles and microvascular complications.

The finding matters because it suggests an accessible, non-invasive AI-derived muscle fat biomarker could help identify patients at higher risk for diabetic nephropathy, where eGFR was lower in the high-FATi group. Uncertainty remains about causality, generalizability beyond a single older type 2 diabetes-predominant cohort, and whether FATi predicts future progression.
limitation: Cross-sectional single-center design cannot establish causality or temporal role of IMF in complications; authors note need for longitudinal and multicentre validation.
tag: Evidence-backed gain
key_points: 120 outpatients from a tertiary Endocrinology and Nutrition Department were studied, mean age 70.8 ± 10.3 years, diabetes duration 13.8 ± 9.6 years, 79.2% type 2 DM. | Ultrasound images were analysed using the PIIXMED AI-system (DAWAKO MedTech; Valencia, Spain) to quantify muscle (Mi) and fat (FATi) percentages. | Highest FATi quartile >43% had higher prevalence of diabetic nephropathy (44.0 vs. 14.7%, p = 0.001) and lower eGFR (63.6 ± 21.3 vs. 72.8 ± 20.1 mL/min/1.73m2, p = 0.045).
rundown: The study enrolled 120 outpatients (57.5% men) with mean HbA1c 8.1% ± 1.5% and assessed anthropometrics, bioimpedance, and rectus femoris ultrasound analyzed by PIIXMED AI-system to derive FATi as intramuscular fat.

Patients in the highest FATi quartile showed more microvascular complications and reduced kidney function, with multivariate analysis showing FATi independently associated with nephropathy with OR 6.01, 95% CI 1.99-18.14.
sources:
- peer_reviewed | Diabetes, Obesity and Metabolism | https://doi.org/10.1111/dom.71249 | 2026-08-24
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