Grune Elena, Nattenmüller Johanna, Kiefer Lena S, Machann Jürgen, Peters Annette, Bamberg Fabian, Schlett Christopher L, Rospleszcz Susanne
Department of Diagnostic and Interventional Radiology, Medical Center, Faculty of Medicine, University of Freiburg, Freiburg, Germany; Institute of Epidemiology, Helmholtz Munich, Neuherberg, Germany; Pettenkofer School of Public Health, LMU Munich, Munich, Germany.
Department of Diagnostic and Interventional Radiology, Medical Center, Faculty of Medicine, University of Freiburg, Freiburg, Germany; Institute for Radiology and Nuclear Medicine Hirslanden Clinic St. Anna, Lucerne, Switzerland.
Metabolism. 2025 Mar;164:156130. doi: 10.1016/j.metabol.2024.156130. Epub 2024 Dec 30.
For characterizing health states, fat distribution is more informative than overall body size. We used population-based whole-body magnetic resonance imaging (MRI) to identify distinct body composition subphenotypes and characterize associations with cardiovascular disease (CVD) risk.
Bone marrow, visceral, subcutaneous, cardiac, renal, hepatic, skeletal muscle and pancreatic adipose tissue were measured by MRI in n = 299 individuals from the population-based KORA cohort. Body composition subphenotypes were identified by data-driven k-means clustering. CVD risk was calculated by established scores.
We identified five body composition subphenotypes, which differed substantially in CVD risk factor distribution and CVD risk. Compared to reference subphenotype I with favorable risk profile, two high-risk phenotypes, III&V, had a 3.8-fold increased CVD risk. High-risk subphenotype III had increased bone marrow and skeletal muscle fat (26.3 % vs 11.4 % in subphenotype I), indicating ageing effects, whereas subphenotype V showed overall high fat contents, and particularly elevated pancreatic fat (25.0 % vs 3.7 % in subphenotype I), indicating metabolic impairment. Subphenotype II had a 2.7-fold increased CVD risk, and an unfavorable fat distribution, probably smoking-related, while BMI was only slightly elevated. Subphenotype IV had a 2.8-fold increased CVD risk with comparably young individuals, who showed high blood pressure and hepatic fat (17.7 % vs 3.0 % in subphenotype I).
Whole-body MRI can identify distinct body composition subphenotypes associated with different degrees of cardiometabolic risk. Body composition profiling may enable a more comprehensive risk assessment than individual fat compartments, with potential benefits for individualized prevention.
在描述健康状况时，脂肪分布比总体体型更具信息量。我们使用基于人群的全身磁共振成像（MRI）来识别不同的身体成分亚表型，并描述其与心血管疾病（CVD）风险的关联。
通过MRI测量了基于人群的KORA队列中n = 299名个体的骨髓、内脏、皮下、心脏、肾脏、肝脏、骨骼肌和胰腺脂肪组织。通过数据驱动的k均值聚类识别身体成分亚表型。通过既定评分计算CVD风险。
我们识别出五种身体成分亚表型，它们在CVD危险因素分布和CVD风险方面有很大差异。与具有有利风险特征的参考亚表型I相比，两种高风险表型III和V的CVD风险增加了3.8倍。高风险亚表型III的骨髓和骨骼肌脂肪增加（亚表型I中为26.3%，而亚表型I中为11.4%），表明存在衰老效应，而亚表型V显示总体脂肪含量高，尤其是胰腺脂肪升高（亚表型I中为25.0%，而亚表型I中为3.7%），表明存在代谢损害。亚表型II的CVD风险增加了2.7倍，且脂肪分布不利，可能与吸烟有关，而体重指数仅略有升高。亚表型IV在相对年轻的个体中CVD风险增加了2.8倍，这些个体表现出高血压和肝脏脂肪（亚表型I中为17.7%，而亚表型I中为3.0%）。
全身MRI可以识别与不同程度的心脏代谢风险相关的不同身体成分亚表型。与单个脂肪隔室相比，身体成分分析可能能够进行更全面的风险评估，对个性化预防具有潜在益处。
