Odoemelam Chiemela S, Naz Afreen, Thanaj Marjola, Sorokin Elena P, Whitcher Brandon, Sattar Naveed, Bell Jimmy D, Thomas E Louise, Cule Madeleine, Yaghootkar Hanieh
School of Natural Science, College of Health and Science, University of Lincoln, Lincoln, U.K.
Research Center for Optimal Health, School of Life Sciences, University of Westminster, London, U.K.
Diabetes. 2025 Jul 1;74(7):1168-1183. doi: 10.2337/db24-1103.
We aimed to identify distinct axes of obesity using advanced magnetic resonance imaging (MRI)-derived phenotypes. We used 24 MRI-derived fat distribution and muscle volume measures (UK Biobank; N = 33,122) to construct obesity axes through principal component analysis. Genome-wide association studies were performed for each axis to uncover genetic factors, followed by pathway enrichment, genetic correlation, and Mendelian randomization analyses to investigate disease associations. Four primary obesity axes were identified: 1) general obesity, reflecting higher fat accumulation in all regions (visceral, subcutaneous, and ectopic fat); 2) muscle dominant, indicating greater muscle volume; 3) peripheral fat, associated with higher subcutaneous fat in abdominal and thigh regions; and 4) lower-body fat, characterized by increased lower-body subcutaneous fat and reduced ectopic fat. Each axis was associated with distinct genetic loci and pathways. For instance, the lower-body fat axis was associated with RSPO3 and COBLL1, which are emerging as promising candidates for therapeutic targeting. Disease risks varied across axes; the general obesity axis was correlated with higher risks of metabolic and cardiovascular diseases, whereas the lower-body fat axis seemed to protect against type 2 diabetes and cardiovascular disease. This study highlights the heterogeneity of obesity through the identification of obesity axes and emphasizes the potential to extend beyond BMI in defining and treating obesity for obesity-related disease management.
This study aimed to address potential limitations of BMI by exploring the heterogeneity of obesity using magnetic resonance imaging-derived fat distribution and muscle volume measures. We sought to identify distinct obesity axes and investigate their genetic, metabolic, and disease associations. Four obesity axes were identified (general obesity, muscle dominant, peripheral fat, and lower-body fat), each linked to unique genetic loci, metabolic traits, and disease risks. These findings emphasize the potential to extend beyond BMI in defining and managing obesity, offering a more nuanced framework for understanding and treating obesity-related diseases.
我们旨在利用先进的磁共振成像（MRI）衍生表型来识别肥胖的不同轴。我们使用了24种MRI衍生的脂肪分布和肌肉体积测量指标（英国生物银行；N = 33122），通过主成分分析构建肥胖轴。对每个轴进行全基因组关联研究以揭示遗传因素，随后进行通路富集、遗传相关性和孟德尔随机化分析以研究疾病关联。确定了四个主要的肥胖轴：1）全身肥胖，反映所有区域（内脏、皮下和异位脂肪）中更高的脂肪堆积；2）肌肉主导型，表明更大的肌肉体积；3）外周脂肪，与腹部和大腿区域更高的皮下脂肪相关；4）下半身脂肪，其特征是下半身皮下脂肪增加和异位脂肪减少。每个轴都与不同的基因位点和通路相关。例如，下半身脂肪轴与RSPO3和COBLL1相关，它们正成为有前景的治疗靶点候选基因。疾病风险在各轴之间有所不同；全身肥胖轴与代谢和心血管疾病的较高风险相关，而下半身脂肪轴似乎对2型糖尿病和心血管疾病有保护作用。这项研究通过识别肥胖轴突出了肥胖的异质性，并强调了在定义和治疗肥胖以管理肥胖相关疾病方面超越体重指数（BMI）的潜力。
本研究旨在通过使用磁共振成像衍生的脂肪分布和肌肉体积测量指标探索肥胖的异质性，以解决BMI的潜在局限性。我们试图识别不同的肥胖轴，并研究它们的遗传、代谢和疾病关联。确定了四个肥胖轴（全身肥胖、肌肉主导型、外周脂肪和下半身脂肪），每个轴都与独特的基因位点、代谢特征和疾病风险相关。这些发现强调了在定义和管理肥胖方面超越BMI 的潜力，为理解和治疗肥胖相关疾病提供了一个更细致入微的框架。
