Diagnostic Accuracy of Non-Invasive Tests for MASLD Across Age, Type 2 Diabetes, and Obesity Subgroups: A Multinational Study.
Non-invasive tests (NITs) are widely used to risk-stratify patients with metabolic dysfunction-associated steatotic liver disease (MASLD); however, their performance may vary according to patient characteristics. We evaluated the accuracy of NITs in a large, multinational MASLD cohort across select subpopulations.
We analyzed 18,759 adults with biopsy-confirmed MASLD from 41 countries. NITs included FIB-4, liver stiffness measurement (LSM), and Agile-3+. Diagnostic performance for advanced fibrosis (F3-F4) was measured using AUCs across subgroups defined by age, sex, type 2 diabetes (T2D), obesity, and alcohol use. Subgroup-specific cutoffs were derived.
Advanced fibrosis was present in 37% of patients. Pooled AUCs were 0.79 for FIB-4, 0.83 for LSM, and 0.86 for Agile-3+. FIB-4 accuracy declined with age (AUC 0.70 in ≥65 years vs. 0.79 in <65 years, p<0.0001) and in middle-aged patients with T2D. The LSM performance remained stable across T2D status but was moderately reduced in patients with obesity and, more profoundly, morbid obesity (BMI >35). Sex and alcohol use had minimal impact on AUCs. Age- and T2D-specific FIB-4 cutoffs varied substantially to maintain predefined accuracy (sensitivity or specificity). The cutoffs for LSM also differed based on patients' BMI, with lower diagnostic cutoffs for advanced fibrosis required in non-obese MASLD (sensitivity 80%: 8.8 kPa in lean, 9.0 kPa overweight, 9.6 kPa in obesity, 11.0 kPa in morbid obesity).
Accuracy of non-invasive tests for advanced fibrosis in MASLD is influenced by age, T2D, and obesity. Age-adjusted FIB-4 thresholds may enhance risk stratification. Imaging-based and composite NITs (LSM and Agile-3+) provide more consistent performance across MASLD subpopulations.
We analyzed 18,759 adults with biopsy-confirmed MASLD from 41 countries. NITs included FIB-4, liver stiffness measurement (LSM), and Agile-3+. Diagnostic performance for advanced fibrosis (F3-F4) was measured using AUCs across subgroups defined by age, sex, type 2 diabetes (T2D), obesity, and alcohol use. Subgroup-specific cutoffs were derived.
Advanced fibrosis was present in 37% of patients. Pooled AUCs were 0.79 for FIB-4, 0.83 for LSM, and 0.86 for Agile-3+. FIB-4 accuracy declined with age (AUC 0.70 in ≥65 years vs. 0.79 in <65 years, p<0.0001) and in middle-aged patients with T2D. The LSM performance remained stable across T2D status but was moderately reduced in patients with obesity and, more profoundly, morbid obesity (BMI >35). Sex and alcohol use had minimal impact on AUCs. Age- and T2D-specific FIB-4 cutoffs varied substantially to maintain predefined accuracy (sensitivity or specificity). The cutoffs for LSM also differed based on patients' BMI, with lower diagnostic cutoffs for advanced fibrosis required in non-obese MASLD (sensitivity 80%: 8.8 kPa in lean, 9.0 kPa overweight, 9.6 kPa in obesity, 11.0 kPa in morbid obesity).
Accuracy of non-invasive tests for advanced fibrosis in MASLD is influenced by age, T2D, and obesity. Age-adjusted FIB-4 thresholds may enhance risk stratification. Imaging-based and composite NITs (LSM and Agile-3+) provide more consistent performance across MASLD subpopulations.
Authors
Younossi Younossi, de Avila de Avila, Petta Petta, Hagström Hagström, Kim Kim, Nakajima Nakajima, Crespo Crespo, Castera Castera, Alkhouri Alkhouri, Zheng Zheng, Treeprasertsuk Treeprasertsuk, Ananchuensook Ananchuensook, Shalimar Shalimar, Tsochatzis Tsochatzis, Trivikrama Trivikrama, Balakumaran Balakumaran, Fan Fan, Roberts Roberts, Alswat Alswat, Wai-Sun Wong Wai-Sun Wong, Yilmaz Yilmaz, Dunn Dunn, Francque Francque, Cordie Cordie, Yu Yu, Ekstedt Ekstedt, Boon-Bee Goh Boon-Bee Goh, Oliveira Oliveira, Pessoa Pessoa, Chan Chan, Castellanos Fernandez Castellanos Fernandez, Duseja Duseja, Arab Arab, Papatheodoridis Papatheodoridis, Sebastiani Sebastiani, Villela-Nogueira Villela-Nogueira, D'Ambrosio D'Ambrosio, Lampertico Lampertico, AlNaamani AlNaamani, Holleboom Holleboom, Valsan Valsan, Venu Venu, El-Kassas El-Kassas, Pennisi Pennisi, Shang Shang, Liu Liu, Lee Lee, Kobayashi Kobayashi, Kakizaki Kakizaki, Caussy Caussy, Pearlman Pearlman, Iruzubieta Iruzubieta, Nadeem Nadeem, Cinque Cinque, Neonaki Neonaki, Zoncapé Zoncapé, Yang Yang, Song Song, Dunn Dunn, Gadi Gadi, Yeh Yeh, Kim-Jun Teh Kim-Jun Teh, Mahadeva Mahadeva, Fabian Fabian, Almohsen Almohsen, Leite Leite, Pugliese Pugliese, Vessby Vessby, Xie Xie, Choudhary Choudhary, Friend Friend, Poca Poca, Kawaguchi Kawaguchi, Russo Russo, Gadano Gadano, Diaz Diaz, Singal Singal, Segrestin Segrestin, Gunn Gunn, Mauricio Mauricio, Arrese Arrese, Fracanzani Fracanzani, Lombardi Lombardi, Aghemo Aghemo, Lam Lam, Racila Racila, Alqahtani Alqahtani, Stepanova Stepanova,
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