Abstract
Rationale and objectives
To investigate the association of photon-counting computed tomography (PCCT)-derived abdominal body composition parameters with metabolic abnormality clustering, and to develop and internally validate clinical, imaging, and combined models for risk assessment MATERIALS AND METHODS: We retrospectively enrolled 116 patients undergoing abdominal PCCT, including 61 with metabolic abnormality clustering and 55 without metabolic abnormality clustering. Metabolic abnormality clustering was defined using a study-specific criterion as the presence of ≥2 of the following: hyperglycemia, hypertension, hypertriglyceridemia, and low high-density lipoprotein cholesterol, for risk-oriented identification of concurrent metabolic abnormalities rather than diagnosis of metabolic syndrome. Clinical (age, sex, body mass index [BMI]) and imaging parameters (visceral fat area, visceral adipose tissue [VAT]/subcutaneous adipose tissue [SAT] ratio, skeletal muscle index, lumbar muscle density, and liver-to-spleen ratio) were collected. Univariable and multivariable logistic regression analyses were performed to identify factors independently associated with metabolic abnormality clustering. Three models were developed: a clinical model (age, sex, and BMI), an imaging model (VAT/SAT ratio), and a combined model incorporating the clinical variables and VAT/SAT ratio. Discrimination was assessed using the area under the receiver operating characteristic curve (AUC), with pairwise comparisons by the DeLong test. Clinical utility was explored using decision curve analysis (DCA). Internal validation was performed using 1000 bootstrap resamples. Calibration was assessed using calibration curves, and a nomogram was constructed based on the combined model.Results
BMI (odds ratio [OR] = 1.476, 95% confidence interval [CI]: 1.263-1.779, p Conclusion
The PCCT-derived VAT/SAT ratio was independently associated with metabolic abnormality clustering. A combined model incorporating age, sex, BMI, and VAT/SAT ratio achieved an AUC of 0.841, although its discrimination was not significantly better than that of the clinical model alone. Further multicenter studies with external validation are warranted before clinical application.