Abstract：
BACKGROUND: There 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.
METHODS: In 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.
RESULTS: Model 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).
CONCLUSIONS: Quantitative 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.
METHODS: In 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.
RESULTS: Model 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).
CONCLUSIONS: Quantitative 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.
摘要：
背景：越来越多的证据表明，目前尚未在临床常规中进行评估，在糖代谢受损个体的风险估计中起着重要作用，因为它与胰岛素抵抗的进展有关。随着人工智能的进步，自动化和准确的算法已经变得可行，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]和奥格斯堡地区合作健康研究[KORA])的数据开发并测试了一种全自动深度学习模型,将全身T1加权Dixon磁共振成像的肌骨形成量化为(1)肌内脂肪组织(IMAT;现行标准)和(2)定量骨骼肌(SM)脂肪分数(SMFF).随后，我们调查了这两种方法对它们在基线人口统计学之外的葡萄糖代谢受损的区分和关联(年龄，性别和体重指数[BMI])和心脏代谢危险因素(脂质面板，收缩压,来自KORA研究的无症状个体的吸烟状况和饮酒)。葡萄糖代谢受损定义为空腹血糖受损或糖耐量受损（140-200mg/dL）或普遍存在的糖尿病。
结果：模型性能很高，在内部(NAKO)和外部(KORA)测试集中，IMAT的Dice系数≥0.81，SM的Dice系数≥0.91。在目标人群中（380名KORA参与者：平均年龄53.6±9.2岁，BMI为28.2±4.9kg/m2，男性占57.4%），葡萄糖代谢受损的个体（n=146;38.4％）年龄较大，更可能是男性，并且表现出更高的心脏代谢风险。更高的IMAT（4.5±2.2%与3.9±1.7％）和更高的SMFF（22.0±4.7％vs.与正常血糖对照组（所有P≤0.005）相比，18.9±3.9％）。与IMAT相比，SMFF对葡萄糖代谢受损的辨别能力更好（受试者工作特征曲线下面积[AUC]0.693vs.0.582，95%置信区间[CI][0.06-0.16]；P<0.001），但与BMI无显著差异（AUC0.733vs.0.693，95%CI[-0.09至0.01]；P=0.15）。在单变量逻辑回归中，IMAT（比值比[OR]=1.18，95％CI[1.06-1.32]；P=0.004）和SMFF（OR=1.19，95％CI[1.13-1.26]；P<0.001）与葡萄糖代谢受损的较高风险相关。在对SMFF(OR=1.10,95%CI[1.01-1.19];P=0.028)但对IMAT(OR=1.14,95%CI[0.97-1.33];P=0.11)的基线人口统计学和心脏代谢危险因素进行多变量调整后，该信号仍然稳健。
结论：定量SMFF，但不是IMAT，是葡萄糖代谢受损的独立预测因子，歧视与BMI没有显着差异，使其成为当前既定方法的有希望的替代方案。自动化的方法，如提出的模型可以提供一个可行的选择，为肌肉骨化的机会性筛查，因此，低成本的个性化风险评估解决方案。
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]和奥格斯堡地区合作健康研究[KORA])的数据开发并测试了一种全自动深度学习模型,将全身T1加权Dixon磁共振成像的肌骨形成量化为(1)肌内脂肪组织(IMAT;现行标准)和(2)定量骨骼肌(SM)脂肪分数(SMFF).随后，我们调查了这两种方法对它们在基线人口统计学之外的葡萄糖代谢受损的区分和关联(年龄，性别和体重指数[BMI])和心脏代谢危险因素(脂质面板，收缩压,来自KORA研究的无症状个体的吸烟状况和饮酒)。葡萄糖代谢受损定义为空腹血糖受损或糖耐量受损（140-200mg/dL）或普遍存在的糖尿病。
结果：模型性能很高，在内部(NAKO)和外部(KORA)测试集中，IMAT的Dice系数≥0.81，SM的Dice系数≥0.91。在目标人群中（380名KORA参与者：平均年龄53.6±9.2岁，BMI为28.2±4.9kg/m2，男性占57.4%），葡萄糖代谢受损的个体（n=146;38.4％）年龄较大，更可能是男性，并且表现出更高的心脏代谢风险。更高的IMAT（4.5±2.2%与3.9±1.7％）和更高的SMFF（22.0±4.7％vs.与正常血糖对照组（所有P≤0.005）相比，18.9±3.9％）。与IMAT相比，SMFF对葡萄糖代谢受损的辨别能力更好（受试者工作特征曲线下面积[AUC]0.693vs.0.582，95%置信区间[CI][0.06-0.16]；P<0.001），但与BMI无显著差异（AUC0.733vs.0.693，95%CI[-0.09至0.01]；P=0.15）。在单变量逻辑回归中，IMAT（比值比[OR]=1.18，95％CI[1.06-1.32]；P=0.004）和SMFF（OR=1.19，95％CI[1.13-1.26]；P<0.001）与葡萄糖代谢受损的较高风险相关。在对SMFF(OR=1.10,95%CI[1.01-1.19];P=0.028)但对IMAT(OR=1.14,95%CI[0.97-1.33];P=0.11)的基线人口统计学和心脏代谢危险因素进行多变量调整后，该信号仍然稳健。
结论：定量SMFF，但不是IMAT，是葡萄糖代谢受损的独立预测因子，歧视与BMI没有显着差异，使其成为当前既定方法的有希望的替代方案。自动化的方法，如提出的模型可以提供一个可行的选择，为肌肉骨化的机会性筛查，因此，低成本的个性化风险评估解决方案。
