BackgroundThere is increasing evidence that myosteatosis, which is currently not assessed in clinical routine, plays an important role in risk estimation in individuals with impaired glucose metabolism, as it is associated with the progression of insulin resistance. With advances in artificial intelligence, automated and accurate algorithms have become feasible to fill this gap.MethodsIn this retrospective study, we developed and tested a fully automated deep learning model using data from two prospective cohort studies (German National Cohort [NAKO] and Cooperative Health Research in the Region of Augsburg [KORA]) to quantify myosteatosis on whole‐body T1‐weighted Dixon magnetic resonance imaging as (1) intramuscular adipose tissue (IMAT; the current standard) and (2) quantitative skeletal muscle (SM) fat fraction (SMFF). Subsequently, we investigated the two measures for their discrimination of and association with impaired glucose metabolism beyond baseline demographics (age, sex and body mass index [BMI]) and cardiometabolic risk factors (lipid panel, systolic blood pressure, smoking status and alcohol consumption) in asymptomatic individuals from the KORA study. Impaired glucose metabolism was defined as impaired fasting glucose or impaired glucose tolerance (140–200 mg/dL) or prevalent diabetes mellitus.ResultsModel performance was high, with Dice coefficients of ≥0.81 for IMAT and ≥0.91 for SM in the internal (NAKO) and external (KORA) testing sets. In the target population (380 KORA participants: mean age of 53.6 ± 9.2 years, BMI of 28.2 ± 4.9 kg/m2, 57.4% male), individuals with impaired glucose metabolism (n = 146; 38.4%) were older and more likely men and showed a higher cardiometabolic risk profile, higher IMAT (4.5 ± 2.2% vs. 3.9 ± 1.7%) and higher SMFF (22.0 ± 4.7% vs. 18.9 ± 3.9%) compared to normoglycaemic controls (all P ≤ 0.005). SMFF showed better discrimination for impaired glucose metabolism than IMAT (area under the receiver operating characteristic curve [AUC] 0.693 vs. 0.582, 95% confidence interval [CI] [0.06–0.16]; P < 0.001) but was not significantly different from BMI (AUC 0.733 vs. 0.693, 95% CI [−0.09 to 0.01]; P = 0.15). In univariable logistic regression, IMAT (odds ratio [OR] = 1.18, 95% CI [1.06–1.32]; P = 0.004) and SMFF (OR = 1.19, 95% CI [1.13–1.26]; P < 0.001) were associated with a higher risk of impaired glucose metabolism. This signal remained robust after multivariable adjustment for baseline demographics and cardiometabolic risk factors for SMFF (OR = 1.10, 95% CI [1.01–1.19]; P = 0.028) but not for IMAT (OR = 1.14, 95% CI [0.97–1.33]; P = 0.11).ConclusionsQuantitative SMFF, but not IMAT, is an independent predictor of impaired glucose metabolism, and discrimination is not significantly different from BMI, making it a promising alternative for the currently established approach. Automated methods such as the proposed model may provide a feasible option for opportunistic screening of myosteatosis and, thus, a low‐cost personalized risk assessment solution.

中文翻译：

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肌脂肪变性与葡萄糖代谢受损之间的关联：深度学习全身磁共振成像群体表型分析方法


背景越来越多的证据表明，目前临床常规中尚未评估的肌脂肪变性在葡萄糖代谢受损个体的风险评估中发挥着重要作用，因为它与胰岛素抵抗的进展相关。随着人工智能的进步，自动化和准确的算法已经可以填补这一空白。方法在这项回顾性研究中，我们使用来自两项前瞻性队列研究（德国国家队列 [NAKO] 和合作健康）的数据开发并测试了全自动深度学习模型奥格斯堡地区的研究 [KORA]）通过全身 T1 加权 Dixon 磁共振成像将肌脂肪变性量化为 (1) 肌内脂肪组织（IMAT；当前标准）和 (2) 定量骨骼肌 (SM) 脂肪分数（SMFF）。随后，我们研究了这两种衡量标准，以区分基线人口统计数据（年龄、性别和体重指数 [BMI]）和心脏代谢危险因素（血脂、收缩压、吸烟状况和饮酒）以及与葡萄糖代谢受损的关系。 KORA 研究中的无症状个体。葡萄糖代谢受损被定义为空腹血糖受损或糖耐量受损 (140–200 mg/dL) 或流行的糖尿病。结果模型性能较高，内部 IMAT 的 Dice 系数≥0.81，SM ≥0.91 (NAKO)和外部 (KORA) 测试装置。目标人群（380 名 KORA 参与者：平均年龄 53.6 ± 9.2 岁，BMI 28.2 ± 4.9 kg/m 2 ，57.4% 男性），糖代谢受损的个体（ n = 146； 38.4%）是年龄较大且更有可能是男性，并且表现出较高的心脏代谢风险状况，较高的 IMAT（4.5 ± 2.与血糖正常对照（所有磷≤ 0.005)。 SMFF 比 IMAT 更好地辨别糖代谢受损（受试者工作特征曲线下面积 [AUC] 0.693 vs. 0.582，95% 置信区间 [CI] [0.06–0.16]；磷< 0.001），但与 BMI 没有显着差异（AUC 0.733 vs. 0.693，95% CI [−0.09 至 0.01]；磷= 0.15）。在单变量逻辑回归中，IMAT（比值比 [OR] = 1.18，95% CI [1.06–1.32]；磷= 0.004）和 SMFF（OR = 1.19，95% CI [1.13–1.26]；磷< 0.001）与葡萄糖代谢受损的较高风险相关。对 SMFF 的基线人口统计数据和心脏代谢危险因素进行多变量调整后，该信号仍然强劲（OR = 1.10，95% CI [1.01–1.19]；磷= 0.028），但不适用于 IMAT（OR = 1.14，95% CI [0.97–1.33]；磷= 0.11）。结论定量 SMFF（而非 IMAT）是葡萄糖代谢受损的独立预测因子，并且区分度与 BMI 没有显着差异，使其成为当前已建立方法的有希望的替代方案。所提出的模型等自动化方法可能为肌脂肪变性的机会性筛查提供可行的选择，从而提供低成本的个性化风险评估解决方案。