Share：
Share this content in WeChat
X
[Chinese][PDF]2073
• Original Article •
Application of IDEAL-IQ to quantitatively evaluate fat deposition and iron overload in abdominal parenchymal organs in rats with type 2 diabetes mellitus
NI Yanhui  ZHANG Xiaoming  XIAO Bo 

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. DOI:10.12015/issn.1674-8034.2024.12.021.


[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.
[Keywords] magnetic resonance imaging；IDEAL-IQ；rats；type 2 diabetes mellitus；fat deposition；iron deposition

NI Yanhui1   ZHANG Xiaoming2*   XIAO Bo3*  

1 Department of Radiology, the Affiliated Hospital of North Sichuan Medical College, Nanchong637000, China

2 Sichuan Key Laboratory of Medical Imaging, Nanchong637000, China

3 Department of Medical Imaging, Bishan Hospital of Chongqing Medical University, Chongqing402760, China

Corresponding author: ZHANG X M, E-mail: Cjr.zhxm@vip.163.com XIAO B, E-mail: xiaoboimaging@163.com

Conflicts of interest   None.

ACKNOWLEDGMENTS  ACKNOWLEDGMENTS 2024 Chongqing Science and Health Joint Medical Research Project 2024MSXM165
Received  2024-07-18
Accepted  2024-12-10
DOI: 10.12015/issn.1674-8034.2024.12.021
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. DOI:10.12015/issn.1674-8034.2024.12.021.

[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]
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]
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]
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]
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.

PREV Application of amide proton transfer imaging combined with apparent diffusion coefficient in the differentiation of benign and malignant soft tissue tumors
NEXT Optimization of multi-parameter MRI with flexible design sequences in the saddle area based on artificial intelligence-assisted compressed sensing technology
  


LINK


Tel & Fax: +8610-67113815    E-mail: editor@cjmri.cn