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基础研究
IDEAL-IQ序列定量评估2型糖尿病大鼠腹腔实质脏器脂肪沉积及铁过载的应用研究
倪艳辉 张小明 肖波

Cite this article as: NI Y H, ZHANG X M, XIAO B. Application of IDEAL-IQ to quantitatively evaluate fat deposition and iron overload in abdominal parenchymal organs in rats with type 2 diabetes mellitus[J]. Chin J Magn Reson Imaging, 2024, 15(12): 143-149.本文引用格式:倪艳辉, 张小明, 肖波. IDEAL-IQ序列定量评估2型糖尿病大鼠腹腔实质脏器脂肪沉积及铁过载的应用研究[J]. 磁共振成像, 2024, 15(12): 143-149. DOI:10.12015/issn.1674-8034.2024.12.021.


[摘要] 目的 利用磁共振功能成像非对称回波最小二乘估算法迭代水脂分离(iteraterative decomposition of water and fat with echo asymmetry and least-squares estimation quantitation, IDEAL-IQ)技术无创定量评估2型糖尿病(type 2 diabetes mellitus, T2DM)大鼠肝脏、肾脏及胰腺的脂肪沉积与铁沉积,并研究T2DM大鼠空腹血糖(fasting blood glucose, FBG)、体质量与脂肪沉积、铁沉积之间的关系,同时比较实验组和对照组大鼠肝肾功、血脂以及病理学改变的差异。材料与方法 10只无特定病原体(specific pathogen free, SPF)健康雄性SD大鼠随机进行分组为实验组(n=7)和对照组(n=3)。实验组进行T2DM的模型建立。待实验组成模后,将两组大鼠进行IDEAL-IQ扫描,通过测定两组大鼠的肝脏、胰腺及肾脏的质子密度脂肪分数(proton density fat fraction, PDFF)和横向弛豫率(transverse relaxation rate, R2*)来评估实验组以及对照组大鼠肝脏、胰腺及肾脏的脂肪沉积及铁过载,扫描结束后心脏取血评估肝功、肾功及血脂改变。处死大鼠后取肝脏、肾脏及胰腺做常规HE染色观察细胞的变化、油红O染色观察脂肪沉积、普鲁士蓝铁染色观察铁沉积。实验数据运用SPSS 27.0软件进行统计学分析,通过Pearson相关系数分析大鼠FBG、体质量与各器官PDFF、R2*值之间的相关性。结果 T2DM组SD大鼠的FBG、体质量、PDFF胰腺、PDFF肝脏、PDFF右肾、PDFF左肾、R2*胰腺、R2*肝脏、甘油三酯(TG)、低密度脂蛋白胆固醇(LDL-C)均高于对照组,差异有统计学意义(P<0.05),但两组大鼠在T1 SI胰腺、T1 SI肝脏、T1 SI右肾、T1 SI左肾、T2 SI胰腺、T2 SI肝脏、T2 SI右肾、T2 SI左肾、R2*右肾、R2*左肾、总胆固醇(TC)、高密度脂蛋白胆固醇(HDL-C)、天门冬氨酸氨基转移酶(AST)、丙氨酸氨基转移酶(ALT)、尿素氮(BUN)、肌酐(Cr)等方面比较差异没有统计学意义(P>0.05)。Pearson相关性分析,FBG与PDFF肝脏(r=0.773)、PDFF胰腺(r=0.837)、PDFF右肾(r=0.895)、PDFF左肾(r=0.784)、R2*肝脏(r=0.876)、体质量(r=0.980)均呈正相关(P<0.05)。体质量与PDFF胰腺(r=0.840)、PDFF右肾(r=0.854)、PDFF左肾(r=0.796)、R2*肝脏(r=0.834)、R2*胰腺(r=0.778)均呈正相关(P<0.05)。结论 本实验通过利用MRI IDEAL-IQ技术无创定量评估T2DM大鼠肝脏及胰腺脂肪沉积与铁沉积的含量,并且同时评估了两组大鼠双肾的脂肪含量差异。这项技术有望动态随访新诊断的糖尿病患者,早期评估其肝脏、肾脏以及胰腺脂肪含量和铁含量改变,为指导临床诊断及治疗提供新的方向。
[Abstract] Objectives The MRI iteraterative decomposition of water and fat with echo asymmetry and least-squares estimation quantitation (IDEAL-IQ) technique was utilized to non-invasively and quantitatively assess fat deposition and iron deposition in the liver, kidney and pancreas of rats with type 2 diabetes mellitus (T2DM), as well as to study the relationship between fasting blood glucose (FBG), body weight, and fat deposition and iron deposition in T2DM rats, and to observe the laboratory and pathological alterations between groups.Materials and Methods Ten specific pathogen free (SPF) healthy male SD rats were randomly grouped into subgroups, experimental group (n=7) and control group (n=3). The experimental group was subjected to the establishment of a model of T2DM, after the experimental group was modeled, the two groups of rats were scanned with MRI IDEAL-IQ. The proton density fat fraction (PDFF) and transverse relaxation rate (R2*) of the liver, pancreas and kidney of the two groups of rats were measured to evaluate the fat deposition and iron overload in the liver, pancreas and kidney of the experimental group and the control group, and to assess the changes in liver function, renal function, and lipids by blood sampling from the heart at the end of the scanning process. The liver, kidney, and pancreas were taken at execution for routine HE staining to observe cellular changes, oil red O staining to observe fat deposition, and Prussian blue iron staining to observe iron deposition. The experimental data were statistically analyzed using SPSS 27.0 software, and the Pearson correlation coefficient was used to analyze the correlation between FBG, body weight and PDFF and R2* values of various organs in rats.Results The FBG, body weight, triglycerides (TG), and low-density lipoprotein cholesterol (LDL-C) of SD rats in the T2DM group were higher than those of the control group, and the PDFF of the pancreas, liver, right kidney, and left kidney as well as the R2* of the pancreas and liver were higher than those of the control group, and the differences were statistically significant (P<0.05). However, the differences in T1 signal intensity and T2 signal intensity of the pancreas, liver, and both kidneys were not statistically significant between the two groups of rats, and the differences in R2*, total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), aspartate transaminase (AST), alanine aminotransferase (ALT), blood urea nitrogen (BUN), and creatinine (Cr) of both kidneys were not statistically significant when compared with those of the control group (P>0.05). Pearson's correlation analysis showed that the differences between FBG and PDFF in the liver (r=0.773), PDFF of the pancreas (r=0.837), PDFF of the right kidney (r=0.895), PDFF of the left kidney (r=0.784), R2* of the liver (r=0.876), and body weight (r=0.980) were positively correlated (P<0.05). Body weight was positively correlated with PDFF of the pancreas (r=0.840), PDFF of the right kidney (r=0.854), PDFF of the left kidney (r=0.796), PDFF of the liver (r=0.834), and PDFF of the pancreas (r=0.778) (P<0.05).Conclusions In this experiment, MRI IDEAL-IQ technology was used to non-invasively and quantitatively evaluate the content of fat deposition and iron deposition in the liver and pancreas of T2DM rats, and the difference in fat content in both kidneys of the two groups of rats was also evaluated. This technique is expected to provide a new direction for clinical diagnosis and treatment by dynamically following newly diagnosed diabetes mellitus patients, and assessing changes in liver, kidney, and pancreatic fat and iron content at an early stage.
[关键词] 磁共振成像;IDEAL-IQ;大鼠;2型糖尿病;脂肪沉积;铁沉积
[Keywords] magnetic resonance imaging;IDEAL-IQ;rats;type 2 diabetes mellitus;fat deposition;iron deposition

倪艳辉 1   张小明 2*   肖波 3*  

1 川北医学院附属医院放射科,南充 637000

2 医学影像四川省重点实验室,南充 637000

3 重庆医科大学附属璧山医院医学影像科,重庆 402760

通信作者:张小明,E-mail: Cjr.zhxm@vip.163.com 肖波,E-mail: xiaoboimaging@163.com

作者贡献声明:张小明主要负责设计本研究的具体方案,对稿件的重要内容进行了修改;肖波参与研究方案的设计,对实验进行指导,并对稿件的重要内容进行了修改,获得了2024年重庆市科卫联合医学科研项目基金资助;倪艳辉进行实验,并负责起草和撰写、修改稿件,获取、分析和解释本研究的数据;全体作者都同意发表最后的修改稿,同意对本研究的所有方面负责,确保本研究的准确性和诚信。


基金项目: 2024年重庆市科卫联合医学科研项目 2024MSXM165
收稿日期:2024-07-18
接受日期:2024-12-10
中图分类号:R445.2  R-332 
文献标识码:A
DOI: 10.12015/issn.1674-8034.2024.12.021
本文引用格式:倪艳辉, 张小明, 肖波. IDEAL-IQ序列定量评估2型糖尿病大鼠腹腔实质脏器脂肪沉积及铁过载的应用研究[J]. 磁共振成像, 2024, 15(12): 143-149. DOI:10.12015/issn.1674-8034.2024.12.021.

0 引言

       糖尿病是一种慢性、异质性和系统性疾病,伴有不同程度的胰岛素缺乏和/或胰岛素抵抗。DM人群中2型糖尿病(type 2 diabetes mellitus, T2DM)占90%以上[1, 2]。T2DM的患病率已经达到流行程度,根据2021年国际糖尿病联盟报告,全球大约有5.37亿成年人患糖尿病,预计到2030年,该数字将上升到6.43亿;到2045年将上升到7.83亿[3],而T2DM是糖尿病中最常见的亚型[4]

       T2DM患者由于其长期的糖脂代谢紊乱可导致肝脏、肾脏及胰腺等器官的异位脂肪沉积及铁沉积,可能会加重胰岛素抵抗,进一步加快T2DM的进程,导致糖尿病肾病等疾病的发生。穿刺病理活检是判断内脏脂肪以及铁沉积的金标准,但是其为创伤性检查,难以重复及综合精确检验,临床应用受限。而微量白蛋白等临床检查可能存在滞后性[5]。因此,临床现阶段亟须解决这一难题,为早期无创评估T2DM患者腹腔实质脏器脂肪及铁含量寻找新方案而影像学检查则是一种无创定量或半定量器官脂肪变性及铁沉积的方法,早期常用超声、计算机断层成像技术(computed tomography, CT)。但是超声影像学依赖于操作者的经验及技术、主观性强,因此临床应用较少;CT准确性较高,但因其有辐射,不适合长期随访[6]。MRI是一种无创无辐射的最佳检查方式,MRI非对称回波最小二乘估算法迭代水脂分离(iteraterative decomposition of water and fat with echo asymmetry and least-squares estimation quantitation, IDEAL-IQ)技术,又称T2*修正梯度多回波水脂分析脂肪定量技术,采用多回波水脂分离技术在进行脂肪定量同时根据多回波信号变化曲线来消除组织T2*的干扰,能够精确量化器官脂肪含量及铁含量的百分比[7, 8, 9]。IDEAL-IQ技术通过在脂肪比像和弛豫图像上勾勒感兴趣区(region of interest, ROI),便可自动获得ROI内质子密度脂肪分数(proton density fat fraction, PDFF)和横向弛豫率(transverse relaxation rate, R2*[10, 11, 12],可以在无创、无辐射的条件下定量检测脂肪含量及铁含量,并且可以反复检查,随访患者动态变化[13],或可为临床早期发现肝脏、肾脏及胰腺的功能损伤提供一种新思路[14, 15]。本研究旨在采用IDEAL-IQ技术无创评估内脏脂肪以及铁的含量,实现对内脏器官脂肪及铁含量改变的重复观察,或可在临床上评估T2DM患者病情改变的过程中起到一定的作用。

1 材料与方法

1.1 实验动物分组与造模

       取无特定病原体(specific pathogen free, SPF)健康雄性 SD大鼠10只,大鼠平均周龄3~8周龄,平均体质量160~200 g。实验动物均由川北医学院动物实验中心提供。10只大鼠适应性喂养1周后,按照随机数字表法将大鼠分为实验组(n=7)和对照组(n=3),实验组进行T2DM模型建立,实验组高脂高糖饮食(高脂饲料的配方为:60%常规饲料,20%蔗糖,15%猪油以及5%胆固醇[16]),对照组常规饮食。每周监测空腹血糖(fasting blood glucose, FBG)及体质量,并观察大鼠每天的摄食、饮水及排尿情况。于第6周在空腹12 h后采用链脲佐菌素(streptozotocin, STZ)(25 mg/kg)腹腔注射以制备T2DM模型大鼠,对照组给予相同剂量的柠檬酸钠缓冲液,注射72 h后,测定FBG,若FBG>11.1 mmol/L,代表T2DM大鼠造模成功[16, 17]。若低于则继续予以STZ(35 mg/kg)加量腹腔注射,视情况最多可连续注射3~4次。注射STZ后观察大鼠的摄食、饮水及排尿情况,并测定大鼠FBG。于第7周实验组大鼠成模,FBG均>11.1 mmol/L,继续高脂饮食喂养,每周测FBG、体质量,观察体质量、FBG变化,于14周行MRI扫描。T2DM大鼠典型体征见图1。本研究经川北医学院实验动物学会福利伦理委员会批准(审批号:2024026)。

       本研究为随机对照研究,实验组为T2DM组,对照组为非糖尿病组。研究对象的FBG、体质量、T1WI信号强度、T2WI的信号强度、PDFF及R2*为结局指标。根据本研究预实验结果,T2DM组大鼠的PDFF胰腺为2.11±1.04,而对照组大鼠的PDFF胰腺为-0.18±0.77,设定实验组数量是对照组的2倍。设定α=0.05,β=0.10,N1为实验组样本量,N2为对照组样本量,N1/N2=2,R=0.5。利用PASS 11软件采用两独立样本均数确定法进行样本量估算。当T2DM组大鼠样本量为5例,非糖尿病组大鼠为3例时,power值为0.872,因此本研究的样本量可以满足统计效能和结果可靠性的要求。

图1  T2DM大鼠,毛色发黄、无光泽,弓背卷体,晶状体浑浊(糖尿病白内障表现)。
Fig. 1  T2DM rats with yellowish, matted fur and bow-backed curls, cloudy lens (manifestation of diabetic cataract).

1.2 仪器及方法

       扫描前禁食8~12 h,使用麻醉设备(Midmark, Matrx VME2)异氟烷吸入麻醉(诱导麻醉剂量浓度3%~4%,维持麻醉剂量浓度1%~2%,氧流量500~700 mL/min)。将麻醉状态下SD大鼠放入美国GE公司Discovery MR 750 3.0 T超导高场磁共振扫描机。俯卧位,头先进,轴位扫描,扫描线圈使用大鼠线圈(Magtron INC, Animal Coil, WK601),扫描的序列包括:T1WI脂肪抑制快速扰相梯度回波序列、T2WI脂肪抑制快速自旋回波序列、IDEAL-IQ,扫描参数见表1

表1  MRI扫描序列及参数
Tab. 1  MRI scan sequences and parameters

1.3 图像后处理

       通过GE后处理工作站AW4.4工作站进行图像处理,由两位具有5年影像诊断经验的放射科医生采用双盲的方法独立在大鼠肝脏、胰腺、双肾勾画ROI得到T1信号强度、T2信号强度、脂肪比值PDFF及R2*值。注意事项:(1)避开明显胆管、血管较多的区域,ROI尽量大;(2)连续测量3次,取平均值。

1.4 生化及病理检查

       扫描结束后一周内处死大鼠,用水合氯醛(350 mL/kg)腹腔注射进行麻醉,麻醉成功后,用5 mL注射器心脏取血,静置2 h后,于离心机中离心15 min(1000 r/min),离心后取上层血清送检。分别取肝脏、胰腺、双肾组织,用磷酸盐缓冲溶液漂洗,分为2组,一组立即-80 °C速冻保存,做冰冻切片,行油红O染色;一组行4%多聚甲醛固定,石蜡包埋,做常规切片HE染色及普鲁士蓝染色。

1.5 统计学分析

       实验数据运用IBM SPSS Statistics version 27.0(Armonk, NY, USA)软件进行统计学分析,用Shapiro-Wilk检验用于检验数据分布的正态性,采用独立样本t检验进行组间比较,用(x¯±s)表示。利用Pearson相关系数分析大鼠FBG、体质量与各器官PDFF、R2*值之间的相关性;使用组内相关系数(intra-class correlation coefficient, ICC)评价两名医生独立勾画ROI的一致性。P<0.05为差异有统计学意义。

2 结果

2.1 入组及参数测定结果

       10只大鼠中8只完成MRI扫描,2只因麻醉死亡。8只扫描完成的大鼠中有2只实验组大鼠因出现酮症酸中毒,不宜再进行实验,为减轻其痛苦遵循伦理实行了安乐死,最终6只大鼠处死取材。

       8只大鼠完成扫描(5只实验组,3只对照组),实验组FBG(27.34±1.92)mmol/L,体质量(480.40±28.12)g;对照组FBG(4.73±0.61)mmol/L,体质量(230.00±16.70)g。处死6只大鼠(3只实验组、3只对照组)取血液及肝脏、肾脏、胰腺行实验室及病理学检查。MRI显示大鼠胰腺、肝脏、双肾、脾脏及腹腔空腔脏器的解剖结构(图2)。

图2  实验组SD大鼠MRI图像。2A、2B:T1WI序列图;2C、2D:IDEAL-IQ脂肪比图;2E、2F:T2WI序列图;2G、2H:R2*弛豫图。2B绿色ROI勾画区域为胰腺大致区域。Sp:脾脏,P:胰腺,LK:左肾,RK:右肾,L:肝脏,G:胃。
Fig. 2  MRI images of SD rats in the experimental group. 2A, 2B: T1WI images; 2C, 2D: IDEAL-IQ fat fraction images; 2E, 2F: T2WI images; 2G, 2H: R2* images. The green ROI outlined area in 2B is the approximate area of the pancreas. Sp: spleen; P: pancreas; LK: left kidney; RK: right kidney; L: liver; G: gastric.

2.2 MRI数据

       两名放射科医生对ROI的测量结果的一致性评价,ICC=0.900,表明两者一致性良好(P<0.001)。两组大鼠在T1 SI胰腺、T1 SI肝脏、T1 SI右肾、T1 SI左肾、T2 SI胰腺、T2 SI肝脏、T2 SI右肾、T2 SI左肾、R2*右肾、R2*左肾比较差异没有统计学意义(P>0.05)(图3图4);与对照组比较,实验组PDFF胰腺、PDFF肝脏、PDFF右肾、PDFF左肾、R2*胰腺、R2*肝脏均高于对照组,差异均有统计学意义(P<0.05),详见表2

图3  实验组大鼠IDEAL-IQ脂肪比图(3A)示胰腺PDFF测量值5.03%,胰腺HE染色(×400)图(3B)示胰岛内多发大小不一的类圆形空泡,胰腺油红O染色(×400)图(3C)示多发红染的大小不等的球形小滴。
图4  对照组大鼠IDEAL-IQ脂肪比图(4A)示胰腺PDFF测量值1.65%,胰腺HE染色(×400)图(4B)示视野内未见空泡影,胰腺油红O染色(×400)图(4C)示视野内未见红染颗粒。IDEAL-IQ:非对称回波最小二乘估算法迭代水脂分离;PDFF:质子密度脂肪分数。
Fig. 3  Rats in the experimental group IDEAL-IQ fat ratio graph (3A) shows pancreatic PDFF measurement of 5.03%, pancreatic HE staining (×400) (3B) shows multiple round-like vacuoles of different sizes in the pancreatic islets, pancreatic oil red O staining (×400) (3C) shows multiple red-stained spherical droplets of varying sizes.
Fig. 4  Rats in the control group IDEAL-IQ fat ratio graph (4A) shows pancreatic PDFF measurement of 1.65%, pancreatic HE staining (×400) (4B) shows no vacuolar shadows in the field of view, pancreatic oil red O staining (×400) (4C) shows no red-stained particles in the field of view. IDEAL-IQ: iteraterative decomposition of water and fat with echo asymmetry and least-squares estimation quantitation; PDFF: proton density fat fraction.
表2  糖尿病鼠实验组与对照组组织间信号及PDFF、R2*值比较
Tab. 2  Comparison of inter-tissue signals and PDFF and R2* values between diabetic rat experimental and control group

2.3 实验室检查

       与对照组比较,实验组甘油三酯(triglycerides, TG)、低密度脂蛋白胆固醇(low-density lipoprotein cholesterol, LDL-C)增高,差异有统计学意义(P<0.05),但是总胆固醇(total cholesterol, TC),高密度脂蛋白胆固醇(high-density lipoprotein cholesterol, HDL-C),天门冬氨酸氨基转移酶(aspartate transaminase, AST),丙氨酸氨基转移酶(alanine aminotransferase, ALT),尿素氮(blood urea nitrogen, BUN),肌酐(creatinine, Cr)组间比较差异没有统计学意义(P>0.05),详见表3

表3  糖尿病鼠实验组与对照组各实验室检查之间的比较
Tab. 3  Comparison between various laboratory tests in the diabetic rat experimental group and control group

2.4 FBG、体质量与肝脏及胰腺的PDFF、R2*值的相关性分析

       Pearson相关分析显示,FBG与PDFF肝脏(r=0.773,P=0.024)、PDFF胰腺(r=0.837,P=0.01)、PDFF右肾(r=0.895,P=0.003)、PDFF左肾(r=0.784,P=0.021)、R2*肝脏(r=0.876,P=0.005)、体质量(r=0.980,P<0.001)均呈正相关。体质量与PDFF胰腺(r=0.840,P=0.009),PDFF右肾(r=0.854,P=0.007)、PDFF左肾(r=0.796,P<0.018)、R2*肝脏(r=0.834,P=0.010)、R2*胰腺(r=0.778,P=0.023)均呈正相关,见图5

图5  空腹血糖与肝脏PDFF值(5A)、胰腺PDFF值(5B)的相关性分析。随着空腹血糖升高,糖尿病鼠的肝脏PDFF、胰腺PDFF值越高,呈正相关。PDFF:质子密度脂肪分数。
Fig. 5  Correlation analysis between blood glucose and liver PDFF (5A) and pancreas PDFF (5B) values. As blood glucose increased, the liver PDFF and pancreas PDFF values of diabetic rats were higher and positively correlated. PDFF: proton density fat fraction.

3 讨论

       T2DM是一种慢性代谢性疾病,长期的胰岛素抵抗以及糖脂代谢异常导致其并发症的产生,据统计约40%的糖尿病患者最终会发展为糖尿病肾病,这与糖尿病患者死亡率的增加密切相关[18]。本研究利用IDEAL-IQ技术评估了T2DM大鼠与对照组肝脏、胰腺及肾脏的脂肪沉积及铁沉积之间的差异,并且比较了两组大鼠肝肾功能的差异,实验结果提示两组间AST、ALT、Cr及BUN之间差异无统计学意义,然后实验组肝脏、胰腺的PDFF及R2*高于对照组,实验组肾脏PDFF也高于对照组,提示IDEAL-IQ技术或可先于临床实验室检查发现器官功能的改变。

3.1 T2DM组肝脏、胰腺PDFF及R2*与对照组的差异性分析

       本实验结果表明,T2DM组大鼠肝脏、胰腺的PDFF及R2*均高于对照组,这与先前的研究结果[12, 19, 20, 21]一致。除此之外,部分研究还发现组织器官随着脂肪沉积程度增加,铁沉积程度加重,这其中存在一定的相关性[22]。T2DM患者除了高血糖外,几乎无一例外地表现出脂质动力学的严重紊乱,通常反映为循环游离脂肪酸和TG水平较高,胰岛素抵抗受试者脂肪组织中脂肪酸的酯化和再酯化减少,其促进脂质在非脂肪细胞组织如肌肉和肝脏中的异位积聚[12, 23]。而在肝脏、肾脏等脏器中过多的脂质沉积可能会导致组织的功能障碍,甚至出现细胞死亡,肝脏脂肪性肝炎和肝硬化,胰腺β细胞的功能障碍以及肾脏的功能损伤,这种现象被称为“脂毒性”[24, 25, 26, 27]。在T2DM患者中,已经有很多项研究证明在不同的器官系统中观察到脂质过载,主要累及的器官包括肌肉、肝脏、心脏、肾脏及胰腺[28, 29]。与PENG等[27]的研究结果一致,当前研究的T2DM组TG高于对照组。T2DM患者常糖脂代谢异常,表现为游离脂肪酸升高,从而导致TG升高。本实验病理检查实验组与对照组肝组织比较两组均未发现脂肪沉积及铁沉积,原因可能是活检取材范围较小,而肝脏脂肪变性为弥漫性病变,未能取到病变组织,导致采样误差。

3.2 T2DM组肾脏PDFF及R2*与对照组的差异性分析

       与非糖尿病组对比,T2DM组大鼠双侧肾脏的PDFF值增高,但是肾脏R2*值两组间差异无统计学意义。糖尿病肾病是糖尿病最常见的并发症,已经有越来越多的证据证明糖尿病肾病可能与肾脏脂肪沉积所致有关[21, 27, 30],肾细胞中脂质的沉积与炎症、纤维化有关,可引起蛋白尿、氧化应激以及肾脏脂质代谢紊乱,这其中机制还可能是肾脏中脂肪含量的增加导致血流速度降低和红细胞积聚,减少肾脏灌注,引起肾脏血流动力学改变,从而损伤肾脏加速糖尿病肾病的发生发展[30, 31, 32]。目前,进一步研究还表明去除肾脏中的脂肪可以保护肾脏[33]。PENG等[27]的研究证实T2DM小鼠的肾皮质、肾髓质的平均脂质含量均高于野生对照组,尤其是皮质。最近一项动物研究还表明肾脏PDFF值会随着糖尿病肾病的进展逐渐增高[34]。YANG等[14]的研究中也发现随着糖尿病的进展,肾皮质和髓质的PDFF值与R2*均呈现增加, DM组皮质和髓质的PDFF和R2*值均显著高于对照组,并且发现尿白蛋白正常的糖尿病患者的PDFF和R2*值与健康对照相比显著升高。因此,这项技术或可能更早地发现糖尿病患者的肾功能异常。与这两项研究结果一致,本研究也证实了T2DM组大鼠的双肾PDFF值高于对照组。然而本实验T2DM组与对照组肾脏R2*值差异没有统计学意义,这与之前的研究结果不符,分析这其中的原因之一可能是与ROI测量不准确有关。此外,对于糖尿病患者,临床诊断糖尿病肾病通常具有10年以上的病史[35],以往的研究发现进展为糖尿病肾病的T2DM患者的肾脏中的铁含量增加明显[36],然而本研究中T2DM大鼠模型成模时间不长,因此可能造成肾功能检查以及R2*值差异均无统计学意义。另外,我们还发现非糖尿病组大鼠肾脏病理活检普鲁士蓝染色显示少量的铁沉积,这可能与生理状态下肾脏对铁元素的代谢与调节有关[37],这也可能是两组大鼠肾脏R2*差异无统计学意义的原因之一。

3.3 T2DM组体质量、FBG与器官组织PDFF及R2*的相关性分析

       本研究发现,T2DM组大鼠的体质量与FBG呈高度正相关,而体质量、FBG与肝脏、胰腺、肾脏的PDFF及肝脏、胰腺的R2*值也高度相关。T2DM患者常合并代谢综合征,脂肪组织增加并表达分泌一系列的脂肪因子及细胞因子,比如白介素-6、肿瘤坏死因子-α等,这些细胞因子可能会加重胰岛素抵抗,从而导致过多的脂质在非脂肪细胞组织如肌肉和肝脏等器官中的异位积聚[38, 39, 40]。芬兰的一项研究[41]表明芬兰东部中年男性体内铁储备的增加与血清胰岛素、血糖浓度升高相关。而SAM等[42]的文章中表明血清铁蛋白增加与胰岛素抵抗的程度成正比,这些研究表明体内铁过载在糖尿病的发生发展中起着重要的作用。

3.4 局限性

       本实验是预实验,样本量较小,无法将不同浓度梯度的FBG进行分组,这也是我们研究的不足之处,我们会在后续的实验中将FBG分成不同的浓度梯度,再讨论不同FBG浓度对实验结果的影响;因禁食12 h扫描,大鼠性情暴躁且发生低血糖,导致麻醉意外发生死亡,无法获得完整的病理及生化标本,且脏器脂肪沉积及铁沉积为弥漫性病变,病理取样存在误差;因大鼠为啮齿类动物,喜啃咬,胃肠道气体较多,部分层面图像质量欠佳,可能导致局部ROI不准确。

4 结论

       本实验通过利用MRI IDEAL-IQ技术无创定量评估T2DM大鼠肝脏及胰腺脂肪沉积与铁沉积的含量,证明了该项技术的可行性。MRI IDEAL-IQ技术在评价糖尿病患者器官脂肪沉积及铁沉积的发生发展中起着重要的作用。或许在未来,我们可以早期利用该技术无创定量随访新诊断的糖尿病患者,早期评估肝脏、胰腺及肾脏成分改变,为临床评估糖尿病病程进展以及临床治疗提供一个崭新的方向。

[1]
XIAO B, XU H B, JIANG Z Q, et al. Acute pancreatitis in patients with a medical history of type 2 diabetes mellitus: clinical findings and magnetic resonance imaging characteristics[J]. Pancreas, 2020, 49(4): 591-597. DOI: 10.1097/MPA.0000000000001530.
[2]
AKSHINTALA D, CHUGH R, AMER F, et al. Nonalcoholic fatty liver disease: the overlooked complication of type 2 diabetes[J]. Pract Diabetol, 2019, 27: 18-24.
[3]
ZIMMET P, ALBERTI K G, MAGLIANO D J, et al. Diabetes mellitus statistics on prevalence and mortality: facts and fallacies[J]. Nat Rev Endocrinol, 2016, 12(10): 616-622. DOI: 10.1038/nrendo.2016.105.
[4]
RICHARDSON A, PARK W G. Acute pancreatitis and diabetes mellitus: a review[J]. Korean J Intern Med, 2021, 36(1): 15-24. DOI: 10.3904/kjim.2020.505.
[5]
黄炎, 黄伟, 章爽, 等. 血清NGAL、hs-CRP、CysC和U-mALB对糖尿病肾病早期诊断价值的初步探讨[J]. 实用预防医学, 2017, 24(10): 1168-1171. DOI: 10.3969/j.issn.1006-3110.2017.10.005.
HUANG Y, HUANG W, ZHANG S, et al. Preliminary exploration on value of serum neutrophil gelatinase-associated lipocalin, high-sensitivity C-reaction protein, cystatian C and urine-microalbumin in early diagnosis of diabetic nephropathy[J]. Pract Prev Med, 2017, 24(10): 1168-1171. DOI: 10.3969/j.issn.1006-3110.2017.10.005.
[6]
ZHENG Y H, YANG S S, CHEN X Y, et al. The Correlation between Type 2 Diabetes and Fat Fraction in Liver and Pancreas: a Study using MR Dixon Technique[J/OL]. Contrast Media Mol Imaging, 2022, 2022: 7073647 [2024-04-05]. https://pmc.ncbi.nlm.nih.gov/articles/PMC9822734/. DOI: 10.1155/2022/7073647.
[7]
SHAN B, DING H Y, LIN Q Z, et al. Repeatability and image quality of IDEAL-IQ in human lumbar vertebrae for fat and iron quantification across acquisition parameters[J/OL]. Comput Math Methods Med, 2022, 2022: 2229160 [2024-04-05]. https://pmc.ncbi.nlm.nih.gov/articles/PMC9203175/. DOI: 10.1155/2022/2229160.
[8]
ZHOU F, SHENG B, LV F R. Quantitative analysis of vertebral fat fraction and R2* in osteoporosis using IDEAL-IQ sequence[J/OL]. BMC Musculoskelet Disord, 2023, 24(1): 721 [2024-04-05]. https://pubmed.ncbi.nlm.nih.gov/37697287/. DOI: 10.1186/s12891-023-06846-4.
[9]
GKOTSIS D E, GOTSIS E D, LYMPEROPOULOU G, et al. Determination of the R2* relaxation rate constant for estimating hepatic iron concentration: a customized approach that considers liver fat infiltration[J]. Phys Med, 2020, 76: 150-158. DOI: 10.1016/j.ejmp.2020.06.019.
[10]
COLGAN T J, VAN PAY A J, SHARMA S D, et al. Diurnal variation of proton density fat fraction in the liver using quantitative chemical shift encoded MRI[J]. J Magn Reson Imaging, 2020, 51(2): 407-414. DOI: 10.1002/jmri.26814.
[11]
KITAGAWA T, KOZAKA K, MATSUBARA T, et al. Fat fraction and R2* values of various liver masses: initial experience with 6-point Dixon method on a 3T MRI system[J/OL]. Eur J Radiol Open, 2023, 11: 100519 [2024-02-28]. https://pmc.ncbi.nlm.nih.gov/articles/PMC10440393/. DOI: 10.1016/j.ejro.2023.100519.
[12]
SARMA M K, SAUCEDO A, DARWIN C H, et al. Noninvasive assessment of abdominal adipose tissues and quantification of hepatic and pancreatic fat fractions in type 2 diabetes mellitus[J]. Magn Reson Imaging, 2020, 72: 95-102. DOI: 10.1016/j.mri.2020.07.001.
[13]
REEDER S B, YOKOO T, FRANÇA M, et al. Quantification of liver iron overload with MRI: review and guidelines from the ESGAR and SAR[J/OL]. Radiology, 2023, 307(1): e221856 [2024-06-07]. https://pmc.ncbi.nlm.nih.gov/articles/PMC10068892/. DOI: 10.1148/radiol.221856.
[14]
YANG C, WANG Z, ZHANG J L, et al. MRI Assessment of Renal Lipid Deposition and Abnormal Oxygen Metabolism of Type 2 diabetes Mellitus Based on mDixon-Quant[J]. J Magn Reson Imaging, 2023, 58(5): 1408-1417. DOI: 10.1002/jmri.28701.
[15]
李淑豪, 喻思思, 邹倩, 等. MRI IDEAL-IQ序列评估2型糖尿病患者胰腺铁过载及脂肪化程度的应用价值[J]. 临床放射学杂志, 2019, 38(6): 1042-1047. DOI: 10.3760/cma.j.issn.1674-5809.2017.12.008.
LI S H, YU S S, ZOU Q, et al. The assessment value of MRI IDEAL-IQ sequence in quantitating pancreatic iron overload and fatty degeneration in type 2 diabetes mellitus patients[J]. J Clin Radiol, 2019, 38(6): 1042-1047. DOI: 10.3760/cma.j.issn.1674-5809.2017.12.008.
[16]
LIN J Y, HE Y N, ZHU N, et al. Metformin attenuates increase of synaptic number in the rat spinal dorsal horn with painful diabetic neuropathy induced by type 2 diabetes: a stereological study[J]. Neurochem Res, 2018, 43(12): 2232-2239. DOI: 10.1007/s11064-018-2642-4.
[17]
FURMAN B L. Streptozotocin-induced diabetic models in mice and rats[J/OL]. Curr Protoc, 2021, 1(4): e78 [2023-12-24]. https://currentprotocols.onlinelibrary.wiley.com/doi/10.1002/cpz1.78. DOI: 10.1002/cpz1.78.
[18]
LI K X, JI M J, SUN H J. An updated pharmacological insight of resveratrol in the treatment of diabetic nephropathy[J/OL]. Gene, 2021, 780: 145532 [2024-01-25]. https://www.sci-hub.ee/10.1016/j.gene.2021.145532. DOI: 10.1016/j.gene.2021.145532.
[19]
KIM T H, JEONG C W, JUN H Y, et al. Noninvasive differential diagnosis of liver iron contents in nonalcoholic steatohepatitis and simple steatosis using multiecho Dixon magnetic resonance imaging[J]. Acad Radiol, 2019, 26(6): 766-774. DOI: 10.1016/j.acra.2018.06.022.
[20]
IMAJO K, KESSOKU T, HONDA Y, et al. MRI-based quantitative R2* mapping at 3 tesla reflects hepatic iron overload and pathogenesis in nonalcoholic fatty liver disease patients[J]. J Magn Reson Imaging, 2022, 55(1): 111-125. DOI: 10.1002/jmri.27810.
[21]
ZHANG X, LI Z L, WOOLLARD J R, et al. Obesity-metabolic derangement preserves hemodynamics but promotes intrarenal adiposity and macrophage infiltration in swine renovascular disease[J]. Am J Physiol Renal Physiol, 2013, 305(3): F265-F276. DOI: 10.1152/ajprenal.00043.2013.
[22]
刘晓怡, 温馨, 周翔海, 等. MRI定量分析2型糖尿病合并非酒精性脂肪性肝病肝脂肪变性程度与铁含量的相关性[J]. 中国医学影像学杂志, 2021, 29(10): 1017-1021. DOI: 10.3969/j.issn.1005-5185.2021.10.013.
LIU X Y, WEN X, ZHOU X H, et al. MRI quantitative analysis of correlation between the degree of liver steatosis and iron content in type 2 diabetes patients with non-alcoholic fatty liver disease[J]. Chin J Med Imag, 2021, 29(10): 1017-1021. DOI: 10.3969/j.issn.1005-5185.2021.10.013.
[23]
AHMED B, SULTANA R, GREENE M W. Adipose tissue and insulin resistance in obese[J/OL]. Biomed Pharmacother, 2021, 137: 111315 [2023-12-21]. https://www.sciencedirect.com/science/article/pii/S0753332221001001?via%3Dihub. DOI: 10.1016/j.biopha.2021.111315.
[24]
GUAN J J, FENG Y Q. Quantitative magnetic resonance imaging of brain iron deposition: comparison between quantitative susceptibility mapping and transverse relaxation rate (R2*) mapping[J]. Nan Fang Yi Ke Da Xue Xue Bao, 2018, 38(3): 305-311. DOI: 10.3969/j.issn.1673-4254.2018.03.10.
[25]
FROMENTY B, RODEN M. Mitochondrial alterations in fatty liver diseases[J]. J Hepatol, 2023, 78(2): 415-429. DOI: 10.1016/j.jhep.2022.09.020.
[26]
BANSAL S K, BANSAL M B. Pathogenesis of MASLD and MASH - role of insulin resistance and lipotoxicity[J]. Aliment Pharmacol Ther, 2024, 59(Suppl 1): S10-S22. DOI: 10.1111/apt.17930.
[27]
PENG X G, BAI Y Y, FANG F, et al. Renal lipids and oxygenation in diabetic mice: noninvasive quantification with MR imaging[J]. Radiology, 2013, 269(3): 748-757. DOI: 10.1148/radiol.13122860.
[28]
RAMÍREZ-ALARCÓN K, VICTORIANO M, MARDONES L, et al. Phytochemicals as potential epidrugs in type 2 diabetes mellitus[J/OL]. Front Endocrinol, 2021, 12: 656978 [2024-06-07]. https://pmc.ncbi.nlm.nih.gov/articles/PMC8204854/. DOI: 10.3389/fendo.2021.656978.
[29]
KIM E H, KIM H K, BAE S J, et al. Gender differences of visceral fat area for predicting incident type 2 diabetes in Koreans[J]. Diabetes Res Clin Pract, 2018, 146: 93-100. DOI: 10.1016/j.diabres.2018.09.020.
[30]
YOKOO T, CLARK H R, PEDROSA I, et al. Quantification of renal steatosis in type Ⅱ diabetes mellitus using dixon-based MRI[J]. J Magn Reson Imaging, 2016, 44(5): 1312-1319. DOI: 10.1002/jmri.25252.
[31]
ZHENG L F, QIN R T, RAO Z J, et al. High-intensity interval training induces renal injury and fibrosis in type 2 diabetic mice[J/OL]. Life Sci, 2023, 324: 121740 [2024-07-06]. https://www.sciencedirect.com/science/article/abs/pii/S0024320523003740?via%3Dihub. DOI: 10.1016/j.lfs.2023.121740.
[32]
XU T T, XU X Y, ZHANG L, et al. Lipidomics reveals serum specific lipid alterations in diabetic nephropathy[J/OL]. Front Endocrinol, 2021, 12: 781417 [2024-01-25]. https://pmc.ncbi.nlm.nih.gov/articles/PMC8695735/. DOI: 10.3389/fendo.2021.781417.
[33]
HEINRICH N S, PEDERSEN R P, VESTERGAARD M B, et al. Evaluation of the effects of ezetimibe on albuminuria and kidney fat in individuals with type 2 diabetes and chronic kidney disease[J]. Diabetes Obes Metab, 2023, 25(9): 2605-2615. DOI: 10.1111/dom.15146.
[34]
CHENG J M, LUO W X, PAN J, et al. Renal ectopic lipid deposition in rats with early-stage diabetic nephropathy evaluated by the MR mDixon-Quant technique: association with the expression of SREBP-1 and PPARα in renal tissue[J]. Quant Imaging Med Surg, 2023, 13(7): 4504-4513. DOI: 10.21037/qims-22-1167.
[35]
中华医学会肾脏病学分会专家组. 糖尿病肾脏疾病诊断、预后评估和生物标志物应用专家共识[J]. 中华肾脏病杂志, 2022, 38(8): 771-784. DOI: 10.3760/cma.j.cn441217-20220106-00112.
Chinese Society of Nephrology. Expert consensus on diagnosis, prognosis assessment and application of biomarkers in diabetic kidney disease[J]. Chin J Nephrol, 2022, 38(8): 771-784. DOI: 10.3760/cma.j.cn441217-20220106-00112.
[36]
ZHAO L J, ZOU Y T, ZHANG J L, et al. Serum transferrin predicts end-stage Renal Disease in Type 2 Diabetes Mellitus patients[J]. Int J Med Sci, 2020, 17(14): 2113-2124. DOI: 10.7150/ijms.46259.
[37]
VAN RAAIJ S E G, RENNINGS A J, BIEMOND B J, et al. Iron handling by the human kidney: glomerular filtration and tubular reabsorption both contribute to urinary iron excretion[J]. Am J Physiol Renal Physiol, 2019, 316(3): F606-F614. DOI: 10.1152/ajprenal.00425.2018.
[38]
AMIN M N, HUSSAIN M S, SARWAR M S, et al. How the association between obesity and inflammation may lead to insulin resistance and cancer[J]. Diabetes Metab Syndr, 2019, 13(2): 1213-1224. DOI: 10.1016/j.dsx.2019.01.041.
[39]
RUZE R, LIU T T, ZOU X, et al. Obesity and type 2 diabetes mellitus: connections in epidemiology, pathogenesis, and treatments[J/OL]. Front Endocrinol, 2023, 14: 1161521 [2024-06-07]. https://pmc.ncbi.nlm.nih.gov/articles/PMC10161731/. DOI: 10.3389/fendo.2023.1161521.
[40]
SAKERS A, DE SIQUEIRA M K, SEALE P, et al. Adipose-tissue plasticity in health and disease[J]. Cell, 2022, 185(3): 419-446. DOI: 10.1016/j.cell.2021.12.016.
[41]
TUOMAINEN T P, NYYSSÖNEN K, SALONEN R, et al. Body iron stores are associated with serum insulin and blood glucose concentrations. Population study in 1, 013 eastern Finnish men[J]. Diabetes Care, 1997, 20(3): 426-428. DOI: 10.2337/diacare.20.3.426.
[42]
SAM R M, SHETTY S S, KUMARI N S, et al. Association between iron profile status and insulin resistance in patients with type 2 diabetes mellitus[J]. J Diabetes Metab Disord, 2023, 22(2): 1453-1458. DOI: 10.1007/s40200-023-01268-4.

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