切换至 "中华医学电子期刊资源库"

中华肥胖与代谢病电子杂志 ›› 2020, Vol. 06 ›› Issue (01) : 10 -15. doi: 10.3877/cma.j.issn.2095-9605.2020.01.003

所属专题: 文献

论著

IDEAL-IQ序列对不同程度肥胖患者内脏脂肪的定量研究
陈永光1, 黄家喜1, 马孟杰1, 莫旭凯1, 林铭霞1, 梁建业1, 张冬1, 史长征1,()   
  1. 1. 510632 广州,暨南大学附属第一医院医学影像中心
  • 收稿日期:2020-01-16 出版日期:2020-02-28
  • 通信作者: 史长征
  • 基金资助:
    广州市科技计划项目(201905010003)

Quantitative study on visceral fat in patients with different degrees of obesity by IDEAL-IQ sequence

Yongguang Chen1, Jiaxi Huang1, Mengjie Ma1, Xukai Mo1, Mingxia Lin1, Jianye Liang1, Dong Zhang1, Changzheng Shi1,()   

  1. 1. Medical imaging center, the First Affiliated Hospital of Jinan University, GuangZhou 510632, China
  • Received:2020-01-16 Published:2020-02-28
  • Corresponding author: Changzheng Shi
  • About author:
    Correspongding author: Shi Changzheng, Email:
引用本文:

陈永光, 黄家喜, 马孟杰, 莫旭凯, 林铭霞, 梁建业, 张冬, 史长征. IDEAL-IQ序列对不同程度肥胖患者内脏脂肪的定量研究[J]. 中华肥胖与代谢病电子杂志, 2020, 06(01): 10-15.

Yongguang Chen, Jiaxi Huang, Mengjie Ma, Xukai Mo, Mingxia Lin, Jianye Liang, Dong Zhang, Changzheng Shi. Quantitative study on visceral fat in patients with different degrees of obesity by IDEAL-IQ sequence[J]. Chinese Journal of Obesity and Metabolic Diseases(Electronic Edition), 2020, 06(01): 10-15.

目的

探讨磁共振成像(MRI)中的非对称回波的最小二乘估算法迭代水脂分离序列(IDEAL-IQ)成像技术对不同程度肥胖患者内脏脂肪含量的检测价值。

方法

选取130例于2016年1月至2019年6月期间在暨南大学附属第一医院就诊的肥胖患者作为研究对象,根据体质量指数(BMI)分为超重组(24 kg/m2≤BMI<28 kg/m2)和肥胖组(BMI≥28 kg/m2),均进行IDEAL-IQ成像检查获得各脏器脂肪分数,统计学上主要进行两样本t检验和Pearson相关分析。

结果

130例肥胖患者中超重组(A组)44例,肥胖组(B组)86例,A组和B组患者组内比较,肝左叶与肝右叶间的脂肪分数无统计学差异,B组患者肝脏(t=2.93,P=0.04)和胰腺(t=3.01,P=0.03)的脂肪分数明显高于A组患者,有统计学差异;两组患者在脾脏(t=0.53,P=0.60)、网膜(t=0.71,P=0.48)、皮下的脂肪分数(t=0.50,P=0.62)及皮下脂肪层厚度(t=1.53,P=0.13)均无统计学差异。肝脏(r=0.269,P=0.002)及胰腺(r=0.238,P=0.005)的脂肪含量与BMI呈正相关关系。

结论

肥胖程度越高,肝脏、胰腺沉积的脂肪成分越多,IDEA-IQ技术可以有效定量腹部各脏器的脂肪含量,并能鉴别不同程度肥胖患者脏器脂肪含量的差异。

Objective

To evaluate body fat content in patients with different degrees of obese patients using the iterative decomposition of water and fat with echo asymmetry and least squares estimation quantification sequence (IDEAL-IQ).

Methods

One hundred and thirty obese patients were recruited in this study. According to body mass index (BMI), they were divided into overweight group (24 kg/m2≤BMI<28 kg/m2, group A) and obesity group (BMI≥28 kg/m2, group B), and performed with IDEAL-IQ imaging sequence to acquire the fat fraction in each organ. Two-sample t test and Pearson correlation analysis were performed.

Results

Among the 130 patients, 44 were included in group A and 86 in group B. There was no significant difference in the fat content between the left lobe and the right lobe in group A or group B, the fat contents of liver (t=2.93, P=0.04) and pancreas (t=3.01, P=0.03) in group B were significantly higher than that in group A, with statistical difference. There was no significant difference in fat content between the two groups in spleen (t=0.53, P=0.60), omentum (t=0.71, P=0.48), and subcutaneous tissues (t=0.50, P=0.62), as well as the thickness of subcutaneous fat layer (t=1.53, P=0.13). Fat contents of liver (r=0.269, P=0.002) and pancreas (r=0.238, P=0.005) was positively correlated with BMI.

Conclusions

The higher degree of obesity, the more fat components deposited in liver and pancreas. IDEA-IQ technology can effectively measure the fat content in abdominal organs and identify the differences of fat content in patients with different degrees of obesity.

表1 超重组(A组)与肥胖组(B组)一般资料及测量指标比较
图1 腹部IDEAL-IQ图像。1A和1C示肝脏感兴趣区,1B和1D示胰腺感兴趣区。1A、1B为超重组患者,BMI=27.9 kg/m2,脂肪定量:肝左叶10.66%,肝右叶12.88%,胰体5.19%,胰尾5.85%、1C、1D为肥胖组患者,BMI=48.2 kg/m2,脂肪定量:肝左叶29.24%,肝右叶38.91%,胰体11.16%,胰尾8.52%。
表2 两组患者肝左右叶和各胰腺部位脂肪含量比较
表3 两组患者各型脂肪肝所比例
图2 肥胖患者肝脏、胰腺脂肪含量与BMI的分析散点图
[1]
杨秋婷. 经济学视角下关于人类肥胖现象的分析研究:文献综述[J]. 经济研究导刊, 2018(29): 189-192.
[2]
李亚红,曾青,张俊梅, 等. 肥胖与代谢综合征的研究进展[J]. 慢性病学杂志, 2018, 19(08): 1038-1042.
[3]
中国医师协会外科医师分会肥胖和糖尿病外科医师委员会. 中国儿童和青少年肥胖症外科治疗指南(2019版)[J/CD]. 中华肥胖与代谢病电子杂志, 2019, 5(1): 3-9.
[4]
王丹,滕东伶,路国涛, 等. 胰腺脂肪浸润的研究进展[J]. 中华全科医师杂志, 2018, 17(7): 567-570.
[5]
中华医学会外科学分会甲状腺及代谢外科学组,中国医师协会外科医师分会肥胖和糖尿病外科医师委员会. 中国肥胖及2型糖尿病外科治疗指南(2019版)[J]. 中国实用外科杂志, 2019, 39(4): 301-306.
[6]
Deng J, Fishbein MH, Rigsby CK, et al. Quantitative MRI for hepatic fat fraction and T2* measurement in pediatric patients with non-alcoholic fatty liver disease[J]. Pediatr Radiol, 2014, 44(11): 1379-1387.
[7]
Yu H, Shimakawa A, Hines CD, et al. Combination of complex-based and magnitude-based multiecho water-fat separation for accurate quantification of fat-fraction[J]. Magn Reson Med, 2011, 66(1): 199-206.
[8]
林铭霞,莫绪凯,林志超, 等. IDEAL-IQ技术诊断OSAS患者非酒精性脂肪性肝病[J]. 暨南大学学报(自然科学与医学版), 2018, 39(5): 454-460.
[9]
Petta S, Gastaldelli A, Rebelos E, et al. Pathophysiology of Non Alcoholic Fatty Liver Disease[J]. Int J Mol Sci, 2016, 17(12): E2082.
[10]
Lopez-Jaramillo P, Gomez-Arbelaez D, Lopez-Lopez J, et al. The role of leptin/adiponectin ratio in metabolic syndrome and diabetes[J]. Horm Mol Biol Clin Investig, 2014, 18(1): 37-45.
[11]
Angulo P. Nonalcoholic fatty liver disease[J]. N Engl J Med, 2002, 346(16): 1221-1231.
[12]
Finkelstein EA, Trogdon JG, Cohen JW, et al. Annual medical spending attributable to obesity: payer-and service-specific estimates[J]. Health Aff (Millwood), 2009, 28(5): w822-w831.
[13]
Wang Y, Beydoun MA, Liang L, et al. Will all Americans become overweight or obese? estimating the progression and cost of the US obesity epidemic[J]. Obesity (Silver Spring), 2008, 16(10): 2323-2330.
[14]
Preiss D, Sattar N. Non-alcoholic fatty liver disease: an overview of prevalence, diagnosis, pathogenesis and treatment considerations[J]. Clin Sci (Lond), 2008, 115(5): 141-150.
[15]
Wieland AC, Mettler P, McDermott MT, et al. Low awareness of nonalcoholic fatty liver disease among patients at high metabolic risk[J]. J Clin Gastroenterol, 2015, 49(1): e6-e10.
[16]
Ou HY, Wang CY, Yang YC, et al. The association between nonalcoholic fatty pancreas disease and diabetes[J]. PLoS One, 2013, 8(5): e62561.
[17]
van Geenen EJ, Smits MM, Schreuder TC, et al. Nonalcoholic fatty liver disease is related to nonalcoholic fatty pancreas disease[J]. Pancreas, 2010, 39(8): 1185-1190.
[18]
贾国瑜. 对脂肪胰的研究[J]. 中国糖尿病杂志, 2014, 22(08): 762-765.
[19]
Alempijevic T, Dragasevic S, Zec S, et al. Non-alcoholic fatty pancreas disease[J]. Postgrad Med J, 2017, 93(1098): 226-230.
[20]
Dietrich P, Hellerbrand C. Non-alcoholic fatty liver disease, obesity and the metabolic syndrome[J]. Best Pract Res Clin Gastroenterol, 2014,28(4):637-653.
[21]
Pettit JE. Spleen function[J]. Clin Haematol, 1977, 6(3): 639-656.
[22]
Herrera MF, Pantoja JP, Velazquez-Fernandez D, et al. Potential additional effect of omentectomy on metabolic syndrome, acute-phase reactants, and inflammatory mediators in grade III obese patients undergoing laparoscopic Roux-en-Y gastric bypass: a randomized trial[J]. Diabetes Care, 2010, 33(7): 1413-1418.
[23]
Kotronen A, Yki-Jarvinen H, Sevastianova K, et al. Comparison of the relative contributions of intra-abdominal and liver fat to components of the metabolic syndrome[J]. Obesity (Silver Spring), 2011, 19(1): 23-28.
[24]
Bondini S, Kleiner DE, Goodman ZD, et al. Pathologic assessment of non-alcoholic fatty liver disease[J]. Clin Liver Dis, 2007, 11(1): 17-23.
[25]
陈晓琼,廖锦堂,余习蛟. 非酒精性脂肪性肝病肝右叶不同深度ARFI测量的比较[J]. 中国超声医学杂志, 2016, 32(07): 616-618.
[26]
Kim SY, Kim H, Cho JY, et al. Quantitative assessment of pancreatic fat by using unenhanced CT: pathologic correlation and clinical implications[J]. Radiology, 2014, 271(1): 104-112.
[27]
马静,董海鹏,宋琼, 等. 双能CT扫描参数和算法对定量检测伴脂肪沉积肝脏铁含量影响的实验研究[J]. 中华放射学杂志, 2014, 48(4): 333-336.
[28]
赵爽,李彩英,高凤宵, 等. 3.0T~1H-MRS在正常肝脏与脂肪肝的对照研究[J]. 临床放射学杂志, 2013, 32(02): 202-206.
[29]
Meisamy S, Hines CD, Hamilton G, et al. Quantification of hepatic steatosis with T1-independent, T2-corrected MR imaging with spectral modeling of fat: blinded comparison with MR spectroscopy[J]. Radiology, 2011, 258(3): 767-775.
[30]
Cassidy FH, Yokoo T, Aganovic L, et al. Fatty liver disease: MR imaging techniques for the detection and quantification of liver steatosis[J]. Radiographics, 2009, 29(1): 231-260.
[31]
Hines CD, Yu H, Shimakawa A, et al. T1 independent, T2* corrected MRI with accurate spectral modeling for quantification of fat: validation in a fat-water-SPIO phantom[J]. J Magn Reson Imaging, 2009, 30(5): 1215-1222.
[32]
代岳,王姗,徐慧婷, 等. IDEAL-IQ技术对不同年龄椎体骨髓脂肪含量的定量评价[J]. 中国医学计算机成像杂志, 2017, 23(02): 161-165.
[1] 项文静, 徐燕, 茹彤, 郑明明, 顾燕, 戴晨燕, 朱湘玉, 严陈晨. 神经学超声检查在产前诊断胼胝体异常中的应用价值[J]. 中华医学超声杂志(电子版), 2024, 21(05): 470-476.
[2] 谢峰, 伍玉晗, 赵胜, 杨小红, 王玉波, 石珍, 范建华, 章敏. 产前超声和MRI诊断胎儿硬脑膜窦畸形的联合应用[J]. 中华医学超声杂志(电子版), 2024, 21(03): 275-280.
[3] 庄若语, 杭明辉, 李文华, 张霆, 侯炜. 膝骨关节炎半定量磁共振评分研究进展[J]. 中华关节外科杂志(电子版), 2024, 18(04): 545-552.
[4] 王莉, 曹蕾, 王亚丹, 张伟. Krabbe病1例临床分析并文献复习[J]. 中华妇幼临床医学杂志(电子版), 2024, 20(03): 339-345.
[5] 陈嘉婷, 杜美君, 石冰, 黄汉尧. 母体系统性疾病对新生儿唇腭裂发生的影响[J]. 中华口腔医学研究杂志(电子版), 2024, 18(04): 262-268.
[6] 孟令凯, 李大勇, 王宁, 王桂明, 张炳南, 李若彤, 潘立峰. 袖状胃切除术对肥胖伴2型糖尿病大鼠的作用及机制研究[J]. 中华普外科手术学杂志(电子版), 2024, 18(06): 638-642.
[7] 李猛, 姜腊, 董磊, 吴情, 贾犇黎. 腹腔镜胃袖状切除术治疗肥胖合并2型糖尿病及脂肪胰的临床研究[J]. 中华普外科手术学杂志(电子版), 2024, 18(05): 554-557.
[8] 谢丽春, 欧庆芬, 张秋萍, 叶升. 简化和标准肝脏MRI方案在结直肠癌肝转移患者随访中的临床应用[J]. 中华普外科手术学杂志(电子版), 2024, 18(04): 434-437.
[9] 杨波, 胡旭, 何金艳, 谢铭. 腹腔镜袖状胃切除术管胃固定研究现状[J]. 中华普外科手术学杂志(电子版), 2024, 18(04): 452-455.
[10] 吉顺富, 汤晓燕, 徐进. 腹腔镜近端胃癌根治术中拓展胃后间隙在肥胖患者中的应用研究[J]. 中华普外科手术学杂志(电子版), 2024, 18(04): 393-396.
[11] 臧书芹, 陈巧玲, 江思源, 朱晓明, 沈浮, 王颢, 张卫, 邵成伟. 基于直肠高分辨MRI的直肠侧系膜分析及其临床价值的研究[J]. 中华结直肠疾病电子杂志, 2024, 13(04): 312-320.
[12] 吴浩凡, 刘元豪, 张锋敏, 张现中, 朱金浩, 黄嘉莹, 刘忠臣, 丁良福, 庄成乐. 基于术前MRI的盆底解剖参数对超低位直肠癌精准功能保肛手术时间的影响[J]. 中华结直肠疾病电子杂志, 2024, 13(03): 209-216.
[13] 唐小久, 胡曼, 许必君, 肖亚. 肥胖合并胃食管反流病患者严重程度与其焦虑抑郁及营养状态的相关性研究[J]. 中华消化病与影像杂志(电子版), 2024, 14(04): 360-364.
[14] 王星, 陈园, 热孜万古丽·乌斯曼, 郭艳英. T2DM、Obesity、NASH、PCOS共同致病因素相关的分子机制[J]. 中华临床医师杂志(电子版), 2024, 18(05): 481-490.
[15] 姜超, 夏旭东, 王功夏, 何向宇, 王海彬, 李媛. 磁共振DWI及其ADC对乳腺导管原位癌伴微浸润腋窝淋巴结转移的诊断价值[J]. 中华介入放射学电子杂志, 2024, 12(03): 234-243.
阅读次数
全文


摘要