Quantification of Ectopic Fat Fractions in Type 2 Diabetes Mellitus using MR-Dixon Technique

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Elsayed, Pasant M. Abo-Elhoda, Nouran M. Said, Aliaa S. Sheha This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5656230/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 09 Jul, 2025 Read the published version in Egyptian Journal of Radiology and Nuclear Medicine → Version 1 posted You are reading this latest preprint version Abstract Background: Type 2 diabetes mellitus (T2DM), a condition affecting over 366 million individuals by 2030, is intimately associated with obesity, insulin resistance, and the accumulation of ectopic fat, particularly in the liver and pancreas. MRI Dixon, a superior imaging technique, offers enhanced assessment of pancreatic fat compared to traditional ultrasound methods. This study aimed to evaluate ectopic fat accumulation in the pancreas, liver, and paraspinal skeletal muscles in T2DM patients compared to healthy controls using MRI Dixon technique. Methods: Using a 3T MRI with the m-Dixon sequence, the liver and pancreas of 15 T2DM patients and 15 healthy volunteers. We measured pancreatic fat fraction (PFF), hepatic fat fraction (HFF), body mass index (BMI), and fat fractions in visceral adipose tissue (VAT), and subcutaneous adipose tissue (SAT). We compared these parameters between the experimental and control groups and analyzed correlations between PFF and other indicators. Results: Diabetic participants exhibited a significantly higher PFF (11.74 ± 3.46) compared to controls (3.61 ± 2.60; p = 0.000). Other fat measurements, including SAT, visceral fat index (VFI), psoas muscle fat fraction (PS FF), and bone marrow fat fraction (BM FF), were also elevated in diabetics (p < 0.05). Conclusion: Pancreatic fat is a crucial indicator of T2DM, showing superior predictive performance relative to other fat measures. Higher levels of liver and visceral fat correlate with poor glycemic control, underscoring the importance of managing blood glucose levels. Type 2 Diabetes Mellitus (T2DM) Ectopic Fat MRI Dixon Glycemic Control Figures Figure 1 Figure 2 Figure 3 Figure 4 Background Diabetes mellitus (DM) is a chronic metabolic disease that is developing at an alarming rate worldwide and alters almost all aspects of the body's metabolism. 1 It is estimated that by 2030, approximately 366 million people will have diabetes, of which more than 90% will have type 2 diabetes mellitus T2DM (T2DM). 2 The prevalence of T2DM in obese adults is 3 to 7 times higher than in adults of normal weight. 3 Two hallmarks of T2DM are insulin resistance (IR) and destruction of pancreatic beta cells, disturbing insulin production. 4 Obesity can lead to excessive ectopic fat infiltration in many organs, including the liver, heart, pancreas, skeletal muscle, and kidney, leading to serious metabolic and clinical consequences. 5 Recent studies discovered that ectopic fat infiltration decreased with weight loss. It is associated with reversal of T2DM, improvement of insulin sensitivity and normalization of glucose metabolism. 6,7 Until now, T2DM was considered an inevitable and progressive process. However, it is now considered a potentially reversible metabolic condition caused by excessive chronic ectopic fat accumulation. MR Dixon has been shown to be superior to ultrasound in assessing pancreatic fat content, and its fat fraction (FF) results can be used as an indicator of pancreatic secretory insufficiency. 8,9 This study aimed to quantify ectopic fat accumulation in pancreas, liver, viscera, and paraspinal skeletal muscles in patients with T2DM compared to healthy controls using fat fraction by MR Dixon technique. Methods Patient Population: This case-control study included 15 patients with T2DM and 15 healthy controls, conducted over 8 months (January–August 2024). Cases for this study were recruited from outpatient clinics of the internal medicine hospital while the controls were recruited from Department of Gastroenterology and Hepatology and from the outpatient clinic, Ain Shams University hospital the outpatient clinics referred for a MRI for reasons other than pancreatic pathology at the department of Radiology, Ain Shams University Hospitals. Inclusion Criteria: Adults (>18 years), laboratory-confirmed T2DM, and controls with normal HbA1c and blood sugar levels. No sex bias. Exclusion Criteria: Type 1 diabetes, prediabetes, chronic alcohol use, iron overload, frequent blood transfusions, chronic pancreatitis, pancreatic cancer, or conditions affecting pancreatic fat deposition. Patients who had any previous history of endocrine pathology involving cortisol, growth hormone, sex steroids and thyroid hormones, or any hormonal replacement therapy by history were excluded. Additionally, patients with MR contraindications or poor image quality for fat content analysis were excluded. Ethical Considerations: The study was approved by the Ethics Committee (approval no. FMASU MS193/2024). An informed consent explaining the procedure details was obtained prior to the study. The privacy of participants and confidentiality of data were guaranteed during the study. Clinical data and assessment : The clinical data, including medications received by all participants were collected. BMI was calculated by dividing weight in kilo grams by square of the height in meters. The accepted BMI ranges were used: underweight– less than 18.5 kg/m 2 , normal weight from 18.5 to 24.9, overweight from 25 to 29.9 and obese ≥30 kg/m 2 . 10 Waist circumference (WC) is a simple and practical measure for assessing central obesity. The International Diabetes Federation (IDF) defines central obesity as a WC of ≥94 cm for men and ≥80 cm for women. 11 According to the American Diabetes Association, DM can be diagnosed if any of the following conditions are met: a fasting plasma glucose level of 126 mg/dL (7.0 mmol/L) or higher, a 2-hour plasma glucose level of 200 mg/dL (11.1 mmol/L) or higher during an Oral Glucose Tolerance Test, a random plasma glucose level of 200 mg/dL (11.1 mmol/L) or higher in individuals with symptoms of hyperglycemia and HbA1c of greater than 6.4%. HbA1c levels reflect different degrees of diabetes DM control. 12 Lipid profile analysis is considered abnormal if TG level exceeded 150 mg/dL, total cholesterol > 200 mg/dl and low-density lipoprotein cholesterol (LDL-C) > 130 mg/dl. In addition to reduced HDL cholesterol level being less than 40 mg/dL for men and less than 50 mg/dL for women. 13 Diagnostic Criteria for Metabolic Syndrome (METS) is identified when a person has at least three of the following five criteria: Abdominal Obesity, Elevated Triglycerides, Low HDL Cholesterol, Hypertension, and elevated Glucose level. 13 MR Imaging Acquisition: Machine: Abdominal MRI exams were performed on a 3 T Achieva; Philips medical system with body coil. Scan Time: 5 - 10 minutes. Subjects’ preparation: Fasting for 4-6 hours as it reduces bowel motion and gas, which can interfere with image quality. For MRI sequences : Axial and coronal T2 WI for rapid acquisition images with an anatomical overview. T2* WI to estimate liver iron concentration. A cut off value < 2mg/g is considered normal. The MR-Dixon technique applied for fat quantification is a non-contrast imaging method, six echo times were collected and R2* and fat-fraction images were generated automatically on the scanner. The acquisition was performed in a single breath-hold of 17 s, to reduce the motion artifacts, during the data acquisition. Table (1) shows the sequence acquisition parameters. Image analysis: Measurement of Fat Fractions (FF) : Manually selecting a specific region of interest (ROIs) in the target organs, the numerical values of the signal intensity (SI) on the generated fat and water images were obtained. Based on the acquired signal intensity values, the content of FF in individual organs was calculated. To measure pancreatic fat and water signal three ROIs were placed in the head, body, and tail of the pancreatic parenchyma avoiding extra-pancreatic adipose tissue. Pancreatic duct and vessels were not included in the measurement, the average value of which was considered as the FF of whole pancreas. For liver, eight ROIs were depicted in each of segment, by avoiding bile ducts, large vessels, lesions, the border of the liver and image artifacts. Measurement of Abdominal Fat Area through the abdominal subcutaneous adipose area (SAT) and the visceral adipose tissue area (VAT) using the FF images. VAT was defined as intra-abdominal fat (including intraperitoneal and retroperitoneal fat) bound by parietal peritoneum or transversalis fascia, excluding the vertebral column and the paraspinal muscles. SAT was defined as fat superficial to the abdominal and back muscles. 3-point Dixon acquisition was prescribed at the level of the L3-L4 intervertebral space to estimate subcutaneous and visceral fat areas in this slice. Segmentation was done on Digital Imaging and Communications in Medicine (DICOM) images following anonymization. MRI of all included patients will be segmented on CoreSlicer platform, accessed via https://coreslicer.com/. It measures subcutaneous fat area and visceral fat area using an artificial intelligence model that performs a ROI analysis based on intensity range. The visceral fat index (VFI = amount of visceral fat/amount of visceral and subcutaneous fat) was calculated. Two additional ROIs were drawn on the paraspinal muscles bilaterally at the level of the third lumbar vertebra. The average FF for muscles was the arithmetic mean of the FF in the right and left paraspinal muscle. Patients: Cases were initially categorized into two groups, T2DM and healthy controls. Pancreatic, hepatic, visceral and skeletal muscle (bilateral paraspinal muscles at lumbar 3 vertebra level) FF values were compared in both groups to find any significant differences. After that the FF values of the cases were compared to each other to detect any significant differences between their values correlating to their disease duration, age, body mass index and degree of disease control. Cases’ laboratory findings were collected to track disease progression and diabetic control, and correlated with FF to evaluate the m-Dixon MRI technique's ability to monitor these factors. Statistical Analysis The collected data was revised, coded, tabulated, and introduced to Statistical package for Social Science (SPSS 21). Data was presented and suitable analysis was done according to the type of data obtained for each parameter. Descriptive statistics: Mean and Standard deviation for numerical data. Frequency and percentage of non-numerical data. Analytical statistics: Student-t test was used to assess the statistical significance of the difference between two study group means. Chi-Square test was used to examine the relationship between two qualitative variables. Correlation analysis (using Spearman’s rho) was used to assess the strength of association between two quantitative variables. Mann Whitney Test (U test) was used to assess the statistical significance of the difference of a non-parametric variable between two study groups. The correlation coefficient denoted symbolically "r" defines the strength (magnitude) and direction (positive or negative) of the linear relationship between two variables. P value: level of significance P > 0.05: Non-significant (NS). P < 0.05: Significant (S). Results This study analyzed 30 cases, including 15 diabetic patients and 15 healthy controls. The mean age of participants was 40.27 years, with a mean BMI of 28.17 kg/m², classifying them in the normal and overweight/ obese categories according to WHO standards. The gender distribution showed a higher proportion of males (63.3%) compared to females (36.7%). METS was present in 53.3% of participants. The analysis examined the relationship between DM and several demographic and health factors, including gender, smoking status, METS, hypertension, and Central Obesity. (Table 2) The study compared body composition and biochemical parameters between diabetic and non-diabetic (NDM) groups. The diabetic group had a higher mean BMI compared to the NDM group, yet not statistically significant (p = 0.145). Hemoglobin A1c (HbA1c) levels were significantly higher in the diabetic group indicating poor glycemic control among diabetic individuals (p = 0.000). Lipid profiles showed no significant differences between both groups. However, FF measurements showed significant differences. (Table 3) In the assessment of body composition parameters and T2DM, PFF was the only parameter showing a significant association with T2DM risk, whereas BMI, HFF, and VFI did not exhibit statistically significant effects Table (4). Pancreatic Fat Fraction (PFF): The AUC was 0.960 with a p-value < 0.001. The optimal cutoff for PFF was 6.35, showing 93% sensitivity and 93% specificity. Its high AUC indicated that it was very effective at distinguishing T2DM from controls due to its strong association with pancreatic dysfunction in diabetes. Hepatic Fat Fraction (HFF): The AUC was 0.744 with a p-value of 0.023. The cutoff for HFF was 5.3, with 60% sensitivity and 80% specificity. HFF reflected fat accumulation in the liver, which was a known risk factor for insulin resistance and T2DM. While HFF was moderately effective, it was less precise than PFF. Visceral Fat Index (VFI): The AUC was 0.867 with a p-value of 0.001. The cutoff was 0.31, yielding 80% sensitivity and 93.3% specificity. VFI quantifies fat stored in the abdominal cavity, which was strongly associated with METS and T2DM. Its high AUC suggested it is a strong marker for T2DM due to its direct link with visceral fat's role in insulin resistance. Body Mass Index (BMI): The AUC was 0.653 with a p-value of 0.152. The cutoff is 28.54, with 66.7% sensitivity and specificity. Its lower AUC indicated it was less effective than PFF and VFI in predicting T2DM. Overall, PFF and VFI were the most reliable parameters for distinguishing diabetics from controls due to their strong associations with diabetes-related metabolic changes, while HFF had moderate effectiveness and BMI was less predictive in this context Table (5) and Figure (1). There was a strong negative correlation between glycemic control and HFF, and a medium negative correlation with VFI. In contrast, there was a negative correlation with PFF. The analysis showed a weak negative correlation between BMI and glycemic control. Similarly, there was a negligible correlation with disease duration, with all parameters. Table (6). Regarding body fat composition parameters, there was no statistically significant differences for each parameter (VFI, HFF, PFF, and BMI), between the insulin and oral medication groups, suggesting similar body fat composition profiles across treatment types Table (7). Discussion Regarding diabetic and control groups, the study's sample demonstrated an equal distribution of T2DM across genders, suggesting balanced representation between diabetic and non-diabetic individuals. T2DM showed notable associations with age, hypertension, central obesity, and METS. However, BMI, Smoking and Gender did not exhibit a significant correlation. Also, we observed no correlation between serum triglycerides, total cholesterol, LDL, HDL, and total adipose tissue distribution quantified by MRI in the T2DM patients, consistent with findings by Sarma et al. 14 . Lipid profile findings analysis: By analyzing Lipid profile findings and different body composition parameters for the participants, Triglycerides exhibited strong positive correlations with PFF, VFI, HFF, and Paraspinal Fat Fraction (PSFF) (r = 0.541, r = 0.454, r = 0.483, and r = 0.459, respectively). Similarly, VLDL was significantly correlated with pancreatic fat. The TG/HDL ratio was also significantly associated with pancreatic, hepatic, visceral area fat deposition, reinforcing its role as a predictor of metabolic risk. These findings align with the results of Zhang et al., who reported a strong correlation between HFF and TG (r = 0.306), and a weaker correlation with PFF (r = 0.183), yet his results showed no significantly correlated between intramuscular FF and TG. These findings suggest that hepatic fat may be involved in glycolipid regulation and could serve as a useful marker for identifying individuals at higher risk for cardiometabolic conditions. 15 De novo adipogenesis is a state of chronic inflammation that increases adipocyte lipolysis, leading to ectopic fat accumulation in organs, after exceeding the capacity of SAT causing adipocytes hypertrophy. 16 The accumulation of fat in skeletal muscle, liver and pancreas may contribute to IR, inhibiting glucose uptake in muscle cells, increasing hepatic gluconeogenesis, and decreasing glycogen synthesis. 17 Our findings revealed significant differences in several body composition parameters including PFF, VFI, bone marrow adipose tissue (BMAT) between DM group (11.74%, 0.40, and 48.97%, respectively) compared to the NDM group (3.61%, 0.23%, and 32.75%, respectively). The HFF showed a higher mean in the DM group (8.51 ± 7.20) compared to the NDM group (4.47 ± 3.72), yet this difference did not reach statistical significance in this study (p = 0.064). In agreement with our findings, An et al. reported higher values for VAT, HFF, PFF, and BM FF in the diabetic group (187.89 cm², 4.12%, 13.05%, and 46.56%, respectively) compared to the NDM group (139.95 cm², 3.40%, 6.70%, and 42.91%, respectively). 18 All these parameters showed statistical significance between the two groups. 18 Figures (2), (3) and (4). Pancreatic fat fraction (PFF): Nonalcoholic fatty pancreatic disease (NAFPD) has been associated with IR, and inflammation, which may contribute to glucose metabolism disorders and diabetes. 19 Our findings are consistent with An et al, which identified pancreatic fat as an independent risk factor for T2DM. 18 Similarly, Chan et al found that each percentage increase in pancreatic fat was linked to a 7% rise in the risk of developing diabetes (aHR 1.07; 95% CI, 1.00–1.16; P = 0.063). 20 In this study, PFF significantly increased the risk of T2DM, with an odds ratio of 2.201, suggesting that higher levels of pancreatic fat are strongly associated with increased diabetes risk. An et al additionally reported that PFF ( AUC of 0.787, with a sensitivity and specificity of 75.00% and 77.29%) show higher predictive value than VAT (AUC of 0.685, with a sensitivity of 67.65% and specificity of 63.37%)in differentiating diabetic and non-diabetic individuals, suggesting that PFF is a stronger predictor of T2DM than VAT, which has a moderate ability to differentiate between groups. 18 This findings were further supported by Tirkes et al, illustrating that PFF has a strong predictive value for identifying T2DM with an AUC of 0.85, having a sensitivity of 69% and specificity of 87%. 21 In this study, PFF demonstrated even greater discriminatory power, with an AUC of 0.960 and both sensitivity and specificity at 93% at a lower cutoff of 6.35%. This highlights PFF's strong predictive value for identifying T2DM and its superiority over VAT and other metrics. 22 Glycemic Control: We observed no significant difference between glycemic control and PFF in diabetic groups, however we found that glycemic control was strongly negatively correlated with HFF and moderately negatively correlated with VFI, suggesting that poor glycemic control is more strongly linked to increased liver and visceral fat rather than pancreatic fat. This could be attributed to the irreversible nature of fat deposition in the pancreas, 23 indicating that other factors besides glycemic control may contribute to the regression of pancreatic pathology. The pancreatic steatosis is a potentially reversible condition, significant reductions in pancreatic fat were observed with 8.9% decrease in body weight or a daily caloric intake of 600 kcal. 24,25 Other study conducted by Salman et al, reported that bariatric surgery resulted in notable reductions in BMI, HbA1c, fasting insulin, HOMA-IR, and total VAT volume, while also improving the lipid profile. 26,27 Duration of Diabetes: In this study, there was no statistically significant differences in PFF based on disease duration. In contrast, Yi et al, reportedthatthere was a significant difference in PFF between patients with a disease duration of more than 5 years and those with a 1-5 years duration (P < 0.001), but not less than that, which could be attributed to a smaller sample size in this study. 28 Hepatic fat fraction (HFF) and Visceral fat index (VFI): This study did not find a statistically significant impact of HFF on T2DM risk, aligning with Zheng et al; 29 An et al noted that while T2DM patients had higher HFF, hepatic fat deposition may not independently contribute to T2DM development, and its role could be influenced by visceral fat, which together affect T2DM risk. 18 Previous studies have shown that severe IR and T2DM risk are seen in individuals with hepatic steatosis resulting from unhealthy lifestyles and excess VAT accumulation. 31 Contrary to our findings, Sarma et al. found that increased fat deposition in the liver, pancreas, and viscera was related to IR in T2DM, 14 while Cao et al observed that T2DM and prediabetic patients had higher HFF than those with normal glucose tolerance. 30 HFF and VFI also showed significant discriminatory ability between diabetic and control groups in our study, with AUCs of 0.744 and 0.867, respectively. Yet, these values were slightly lower compared to PFF with AUC of 0.960. However, we suggest the predictive accuracy of HFF may be diminished in patients who are overweight or have METS due to overlapping results, as these conditions can influence fat deposition patterns, leading to decreased test accuracy. Ducluzeau et al demonstrated that patients with T2DM and those with met METS exhibit similar degrees of hepatic steatosis, with HFF values of 16.8% and 15%, respectively, suggesting that liver fat alone may not be sufficient for distinguishing between these conditions. 31 Body mass index (BMI): In our study, BMI was not significantly associated with T2DM risk, yet there was modest correlation with central obesity (r = 0.333, p > 0.05), suggesting that it may play more crucial roles in identifying T2DM risk. Levelt et al reported that T2DM is linked to increased hepatic triglyceride, regardless of obesity. 32 Additionally, our study found non-significant correlations between BMI and VFI, as well as BMI and PFF. This aligns with findings by Gaborit et al , who reported that pancreatic fat is independent of BMI. 33 However, we did observe a moderate positive correlation between BMI and HFF, indicating that higher BMI is linked to greater liver fat, consistent with findings by Chen et al, who also noted positive associations between HFF, BMI, and triglycerides. 34 Moreover, our results revealed strong relationships between HFF and PFF, with VFI. In comparison, Waddell et al found weak associations between VFI and pancreas fat. This suggests that visceral fat is a key driver of ectopic fat deposition in both the liver and pancreas. 35 These findings highlight the importance of reducing visceral and hepatic fat for optimal diabetes management. Effective glycemic plays a key role in mitigating ectopic fat, including fat accumulation in the liver and visceral areas. 36 The strong associations between visceral fat, liver fat, and pancreatic fat emphasize the need for focused strategies to manage these fat compartments in T2DM care. Diabetic medications effect: In this study, there were no statistically significant differences in body fat composition parameters between patients treated with subcutaneous insulin injection and those receiving oral hypoglycemic medications, suggesting that both types of treatment groups had similar body fat profiles. In a randomized trial by Gastaldelli et al , diabetic patients treated with tirzepatide for 52 weeks showed a significant reduction in both liver fat content and VAT volumes compared to those treated with insulin degludec. These findings suggest that liver fat fraction and VAT, could be valuable in future studies assessing the development, progression, and treatment response of liver fat burden in patients with T2DM during larger clinical trials. 37 Study limitations and recommendations: The study faced several limitations, including a small sample size of 30 participants, split into 15 diabetic and 15 non-diabetic individuals, which limited the statistical power of the findings. The small sample size likely contributed to wide confidence intervals, particularly for the VFI. Additionally, the cross-sectional design of the study made it challenging to establish causality between diabetes and body composition parameters. Furthermore, the study relied on single-time measurements of body fat and glycemic control, meaning any changes over time were not considered. Future research should aim to include larger and more diverse samples to enhance the generalizability of the findings. Longitudinal study designs would allow for the examination of changes over time in diabetes status, body fat composition, and glycemic control, providing a better understanding of the causal relationships. Conclusion This study found that Pancreatic, hepatic, and visceral fat levels were significantly higher in diabetics, especially in those with poor glycemic control. Pancreatic fat emerged as a strong indicator of diabetes risk. BMI showed weaker associations with diabetes, and disease duration or treatment type did not impact body fat composition. Further research is needed to confirm these findings and their role in managing T2DM. Quantification of ectopic fat fractions using the MR-Dixon technique offers a non-invasive, accurate, and clinically valuable approach to assess metabolic risk in T2DM. It enhances understanding of disease pathophysiology, aids early detection of complications, and provides a reliable tool for monitoring therapeutic response—thereby supporting precision medicine in the management of Type 2 Diabetes Mellitus. Abbreviations ADA : American Diabetes Association AHA : American Heart Association AT: Adipose Tissue AUC : Area under the Curve BMAT : Bone Marrow Adipose Tissue BMI : Body Mass Index CI : Confidence Interval DICOM : Digital Imaging and Communications in Medicine DM : Diabetes Mellitus FA : Fatty Acid FF : Fat Fraction FFA : Free Fatty Acids HbA1c : Hemoglobin A1c HFF : Hepatic Fat Fraction IDF : International Diabetes Federation IQR : Interquartile Range IR : Insulin Resistance IVC : Inferior Vena Cava LDL-C : Low-Density Lipoprotein Cholesterol MetS : Metabolic Syndrome MR : Magnetic Resonance MRI-PDFF : MRI-Proton Density Fat Fraction NAFLD : Non-Alcoholic Fatty Liver Disease NAFPD : Nonalcoholic Fatty Pancreatic Disease NASH : Non-Alcoholic Steatohepatitis NDM : Non-Diabetic NHLBI : National Heart, Lung, and Blood Institute NS : Non-Significant OGTT : Oral Glucose Tolerance Test OR : Odds Ratio PDFF : Proton Density Fat Fraction PFF : Pancreatic Fat Fraction PI : Pancreatic Index PSFF : Paraspinal Fat Fraction PUFAs : Polyunsaturated Fatty Acids ROC : Receiver Operating Characteristic ROI : Region-of-Interest SAT : Subcutaneous adipose tissue SI : Signal Intensity T2DM : Type 2 Diabetes Mellitus TE : Time Echo TG : Triglycerides VAT : Visceral adipose tissue VFI : Visceral Fat Index VLDL : Very Low-Density Lipoprotein VOI : Voxel of Interest WC : Waist Circumference WHO : World Health Organization References NCD Risk Factor Collaboration (NCD-RisC). 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Frontiers in Medicine, 9, 894465. Wagner, R., Eckstein, S. S., Yamazaki, H., Gerst, F., Machann, J., Jaghutriz, B. A., & Heni, M. (2022). Metabolic implications of pancreatic fat accumulation. Nature Reviews Endocrinology, 18(1), 43-54. Yi, J., Xu, F., Li, T., Liang, B., Li, S., Feng, Q., & Long, L. (2023). Quantitative study of 3T MRI qDixon-WIP applied in pancreatic fat infiltration in patients with type 2 diabetes mellitus. Frontiers in Endocrinology, 14, 1140111. Zheng, Y., Yang, S., Chen, X., Lv, J., Su, J., & Yu, S. (2022). The Correlation between Type 2 Diabetes and Fat Fraction in Liver and Pancreas: A Study using MR Dixon Technique. Contrast Media & Molecular Imaging, 2022, 1–8. Cao, M. J., Wu, W. J., Chen, J. W., Fang, X. M., Ren, Y., Zhu, X. W., & Tang, Q. F. (2023). Quantification of ectopic fat storage in the liver and Pancreas using six-point Dixon MRI and its association with insulin sensitivity and Β-cell function in patients with central obesity. European Radiology, 33(12), 9213-9222. Ducluzeau, P. H., Boursier, J., Bertrais, S., Dubois, S., Gauthier, A., Rohmer, V., Gagnadoux, F., Leftheriotis, G., Cales, P., Andriantsitohaina, R., Roullier, V., & Aubé, C. (2013). MRI measurement of liver fat content predicts the metabolic syndrome. Diabetes & metabolism, 39(4), 314–321. Levelt, E., Pavlides, M., Banerjee, R., Mahmod, M., Kelly, C., Sellwood, J., & Neubauer, S. (2016). Ectopic and visceral fat deposition in lean and obese patients with type 2 diabetes. Journal of the American College of Cardiology, 68(1), 53-63. Gaborit, B., Abdesselam, I., Kober, F., Jacquier, A., Ronsin, O., Emungania, O., Lesavre, N., Alessi, M. C., Martin, J. C., Bernard, M., & Dutour, A. (2015). Ectopic fat storage in the pancreas using 1H-MRS: importance of diabetic status and modulation with bariatric surgery-induced weight loss. International journal of obesity (2005), 39(3), 480–487. Chen, J., Yue, J., Fu, J., He, S., Liu, Q., Yang, M., Zhang, W., Xu, H., Lu, Q., & Ma, J. (2023). A prediction model of liver fat fraction and presence of non-alcoholic fatty liver disease (NAFLD) among patients with overweight or obesity. Endocrine journal, 70(10), 977–985. Waddell, T., Bagur, A., Cunha, D., Thomaides-Brears, H., Banerjee, R.,Cuthbertson, D. J., Brown, E., Cusi, K., Després, J. P., & Brady, M. (2022). Greater ectopic fat deposition and liver fibroinflammation and lower skeletal muscle mass in people with type 2 diabetes. Obesity (Silver Spring, Md.), 30(6),1231–1238. Kazeminasab, F., Bahrami Kerchi, A., Behzadnejad, N., Belyani, S., Rosenkranz, S. K., Bagheri, R., & Dutheil, F. (2024). The Effects of Exercise Interventions on Ectopic and Subcutaneous Fat in Patients with Type 2 Diabetes Mellitus: A Systematic Review, Meta-Analysis, and Meta-Regression. Journal of clinical medicine, 13(17), 5005. Gastaldelli, A., Cusi, K., Fernández Landó, L., Bray, R., Brouwers, B., & Rodríguez, Á. (2022). Effect of tirzepatide versus insulin degludec on liver fat content and abdominal adipose tissue in people with type 2 diabetes (SURPASS-3 MRI): a substudy of the randomised, open-label, parallel-group, phase 3 SURPASS-3 trial. The lancet. Diabetes & endocrinology, 10(6), 393–406. Tables Tables 1 to 7 are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Table1.docx Table2.docx Table3.docx Table4.docx Table5.docx Table6.docx Table7.docx Cite Share Download PDF Status: Published Journal Publication published 09 Jul, 2025 Read the published version in Egyptian Journal of Radiology and Nuclear Medicine → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5656230","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":449900027,"identity":"dcd5a214-b6bb-49df-811d-b2e710b3b35a","order_by":0,"name":"Rouan A. Elsayed","email":"data:image/png;base64,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","orcid":"","institution":"Ain Shams University","correspondingAuthor":true,"prefix":"","firstName":"Rouan","middleName":"A.","lastName":"Elsayed","suffix":""},{"id":449900028,"identity":"7af14c45-adc7-4792-a0eb-0aacc5e3f70c","order_by":1,"name":"Pasant M. Abo-Elhoda","email":"","orcid":"","institution":"Ain Shams University","correspondingAuthor":false,"prefix":"","firstName":"Pasant","middleName":"M.","lastName":"Abo-Elhoda","suffix":""},{"id":449900029,"identity":"b3809a23-3692-4464-8a80-c43457d5b928","order_by":2,"name":"Nouran M. Said","email":"","orcid":"","institution":"Ain Shams University","correspondingAuthor":false,"prefix":"","firstName":"Nouran","middleName":"M.","lastName":"Said","suffix":""},{"id":449900030,"identity":"06bc1ec9-0f5d-41f5-8219-8c78333b5e0c","order_by":3,"name":"Aliaa S. Sheha","email":"","orcid":"","institution":"Ain Shams University","correspondingAuthor":false,"prefix":"","firstName":"Aliaa","middleName":"S.","lastName":"Sheha","suffix":""}],"badges":[],"createdAt":"2024-12-16 18:53:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5656230/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5656230/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s43055-025-01500-6","type":"published","date":"2025-07-09T15:57:29+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":82064648,"identity":"e3b0df18-ec61-4597-ab1d-0b360ad86408","added_by":"auto","created_at":"2025-05-06 12:25:25","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":20821,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating characteristic (ROC) curves \u003cbr\u003e\nof BMI, HFF, VFI and PFF for predicting T2DM.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5656230/v1/b8583ccd1f557cc3e4bdbaaa.jpg"},{"id":82064649,"identity":"885002ba-22cc-4cae-a6e4-a670b17c7e7f","added_by":"auto","created_at":"2025-05-06 12:25:25","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1084540,"visible":true,"origin":"","legend":"\u003cp\u003eA 65-year-old male with \u003cstrong\u003euncontrolled diabetes\u003c/strong\u003e, evidenced by HbA1c of 11.6%. He was on insulin and lipid-lowering treatments. He had a BMI of 32, indicating obesity (A) T2WI of abdomen. Region of interest locations, (B, C) In- Phase and Out-Phase. (D, E) Mean HFF and PFF were 2.6 % and 19.6 % respectively. (F) Mean fat fractions in subcutaneous adipose tissue, paraspinal muscles and lumbar vertebra were measured (94.9 %, 8.4 % and 52.8%) respectively. Visceral fat area and subcutaneous fat areas measured 301 and 209 mm2 respectively, with visceral fat index (0.41).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5656230/v1/bf857d92e591d159840f537a.png"},{"id":82064657,"identity":"472a9781-053b-4cbe-8d3d-ef3bc7582536","added_by":"auto","created_at":"2025-05-06 12:25:25","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1116634,"visible":true,"origin":"","legend":"\u003cp\u003eA 31-year-old male known diabetic for two years duration. His HbA1c was 7.3 % denoting \u003cstrong\u003epoor glycemic control\u003c/strong\u003e. He was on \u003cstrong\u003eoral treatment\u003c/strong\u003e and no lipid-lowering therapy. He had a BMI of 34, indicating obesity. (A) T2WI of abdomen. Region of interest locations, (B, C) In- Phase and Out-Phase. (D, E) Mean HFF and PFF were 27.8 % and 16.9 % respectively. (F) Mean fat fractions in subcutaneous adipose tissue, paraspinal muscles and lumbar vertebra were measured (93 %, 4.9 % and 42.7%) respectively. Visceral fat area and subcutaneous fat areas measured 277 and 290 mm2 respectively, with visceral fat index (0.49).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5656230/v1/6606418c72098a5a38721bdd.png"},{"id":82064658,"identity":"335a3204-ebad-42cc-9d14-132b41e287a4","added_by":"auto","created_at":"2025-05-06 12:25:25","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":971368,"visible":true,"origin":"","legend":"\u003cp\u003eA 32-year-old non-diabetic male with free medical history and BMI of 27.7 and no history of metabolic syndrome underwent an abdominal MRI using T2-weighted sequences. (A) T2WI of abdomen. Region of interest locations, (B, C) In- Phase and Out-Phase. (D, E, F) Mean HFF and PFF were 2.6 % and 2 % respectively. (D) Mean fat fractions in subcutaneous tissue, spinal muscles and lumbar vertebra were measured (88.6 %, 1.3 % and 39.6 %) respectively. Visceral fat area and subcutaneous fat areas measured 57 and 303 mm2 respectively, with visceral fat index (0.15).\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5656230/v1/d915d23582825a8a9043f0c2.png"},{"id":86700083,"identity":"23e5bb4b-5013-43dc-9b35-56c77051ab34","added_by":"auto","created_at":"2025-07-14 16:11:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5028249,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5656230/v1/ac0fb0ef-fac0-4847-aa93-d538334a2c87.pdf"},{"id":82064652,"identity":"f0c0f446-31be-4e30-8ccc-a8a307ecf4a9","added_by":"auto","created_at":"2025-05-06 12:25:25","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":13145,"visible":true,"origin":"","legend":"","description":"","filename":"Table1.docx","url":"https://assets-eu.researchsquare.com/files/rs-5656230/v1/8d4c930ff8e471dc6145cfd3.docx"},{"id":82068788,"identity":"986d9c3b-6c32-4bf0-90db-98758edf3589","added_by":"auto","created_at":"2025-05-06 12:57:25","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":14557,"visible":true,"origin":"","legend":"","description":"","filename":"Table2.docx","url":"https://assets-eu.researchsquare.com/files/rs-5656230/v1/ea76e39fddf170116aa4923d.docx"},{"id":82064650,"identity":"6f9a06f9-853c-4b3d-b8db-166115079391","added_by":"auto","created_at":"2025-05-06 12:25:25","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":15170,"visible":true,"origin":"","legend":"","description":"","filename":"Table3.docx","url":"https://assets-eu.researchsquare.com/files/rs-5656230/v1/8ec85c6e04d7022f53895784.docx"},{"id":82067655,"identity":"7156db1b-9c08-4238-bac3-5e1a83dedfe1","added_by":"auto","created_at":"2025-05-06 12:49:25","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":13506,"visible":true,"origin":"","legend":"","description":"","filename":"Table4.docx","url":"https://assets-eu.researchsquare.com/files/rs-5656230/v1/43a543c19618650f1fd1860c.docx"},{"id":82067118,"identity":"6a141b42-cd76-415e-8219-d0cd4e785f37","added_by":"auto","created_at":"2025-05-06 12:41:25","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":13866,"visible":true,"origin":"","legend":"","description":"","filename":"Table5.docx","url":"https://assets-eu.researchsquare.com/files/rs-5656230/v1/83171fef3f7e957f6ac02c5e.docx"},{"id":82065530,"identity":"883e5038-e893-4dad-893c-dda3c59d38ce","added_by":"auto","created_at":"2025-05-06 12:33:25","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":13411,"visible":true,"origin":"","legend":"","description":"","filename":"Table6.docx","url":"https://assets-eu.researchsquare.com/files/rs-5656230/v1/986030daf4c530503403cce6.docx"},{"id":82064656,"identity":"fa9e44ca-27de-4874-9716-df61e18465aa","added_by":"auto","created_at":"2025-05-06 12:25:25","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":14450,"visible":true,"origin":"","legend":"","description":"","filename":"Table7.docx","url":"https://assets-eu.researchsquare.com/files/rs-5656230/v1/9d3ce00cc708a893c498d44c.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Quantification of Ectopic Fat Fractions in Type 2 Diabetes Mellitus using MR-Dixon Technique","fulltext":[{"header":"Background","content":"\u003cp\u003eDiabetes mellitus (DM) is a chronic metabolic disease that is developing at an alarming rate worldwide and alters almost all aspects of the body\u0026apos;s metabolism.\u003csup\u003e\u0026nbsp;1\u003c/sup\u003e It is estimated that by 2030, approximately 366 million people will have diabetes, of which more than 90% will have type 2 diabetes mellitus T2DM (T2DM).\u003csup\u003e\u0026nbsp;2\u003c/sup\u003e The prevalence of T2DM in obese adults is 3 to 7 times higher than in adults of normal weight.\u003csup\u003e\u0026nbsp;3\u003c/sup\u003e Two hallmarks of T2DM are insulin resistance (IR) and destruction of pancreatic beta cells, disturbing insulin production.\u003csup\u003e\u0026nbsp;4\u003c/sup\u003e Obesity can lead to excessive ectopic fat infiltration in many organs, including the liver, heart, pancreas, skeletal muscle, and kidney, leading to serious metabolic and clinical consequences.\u003csup\u003e\u0026nbsp;5\u003c/sup\u003e Recent studies discovered that ectopic fat infiltration decreased with weight loss. It is associated with reversal of T2DM, improvement of insulin sensitivity and normalization of glucose metabolism.\u003csup\u003e\u0026nbsp;6,7\u0026nbsp;\u003c/sup\u003eUntil now, T2DM was considered an inevitable and progressive process. However, it is now considered a potentially reversible metabolic condition caused by excessive chronic ectopic fat accumulation. MR Dixon has been shown to be superior to ultrasound in assessing pancreatic fat content, and its fat fraction (FF) results can be used as an indicator of pancreatic secretory insufficiency.\u003csup\u003e\u0026nbsp;8,9\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eThis study aimed to quantify ectopic fat accumulation in pancreas, liver, viscera, and paraspinal skeletal muscles in patients with T2DM compared to healthy controls using fat fraction by MR Dixon technique.\u0026nbsp;\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003ePatient Population:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis case-control study included 15 patients with T2DM and 15 healthy controls, conducted over 8 months (January–August 2024). Cases for this study were recruited from outpatient clinics of the internal medicine hospital while the controls were recruited from Department of Gastroenterology and Hepatology and from the outpatient clinic, Ain Shams University hospital the outpatient clinics referred for a MRI for reasons other than pancreatic pathology at the department of Radiology, Ain Shams University Hospitals.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInclusion Criteria:\u003c/strong\u003e Adults (\u0026gt;18 years), laboratory-confirmed T2DM, and controls with normal HbA1c and blood sugar levels. No sex bias. \u003cstrong\u003eExclusion Criteria:\u003c/strong\u003e Type 1 diabetes, prediabetes, chronic alcohol use, iron overload, frequent blood transfusions, chronic pancreatitis, pancreatic cancer, or conditions affecting pancreatic fat deposition. Patients who had any previous history of endocrine pathology involving cortisol, growth hormone, sex steroids and thyroid hormones, or any hormonal replacement therapy by history were excluded. Additionally, patients with MR contraindications or poor image quality for fat content analysis were excluded.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Considerations:\u003c/strong\u003e The study was approved by the Ethics Committee (approval no. FMASU MS193/2024). An informed consent explaining the procedure details was obtained prior to the study. The privacy of participants and confidentiality of data were guaranteed during the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical data and assessment\u003c/strong\u003e: The clinical data, including medications received by all participants were collected. BMI was calculated by dividing weight in kilo grams by square of the height in meters. The accepted BMI ranges were used: underweight– less than 18.5 kg/m\u003csup\u003e2\u003c/sup\u003e, normal weight from 18.5 to 24.9, overweight from 25 to 29.9 and obese ≥30 kg/m\u003csup\u003e2\u003c/sup\u003e.\u003csup\u003e10\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eWaist circumference (WC) is a simple and practical measure for assessing central obesity. The International Diabetes Federation (IDF) defines central obesity as a WC of ≥94 cm for men and ≥80 cm for women.\u003csup\u003e11\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eAccording to the American Diabetes Association, DM can be diagnosed if any of the following conditions are met: a fasting plasma glucose level of 126 mg/dL (7.0 mmol/L) or higher, a 2-hour plasma glucose level of 200 mg/dL (11.1 mmol/L) or higher during an Oral Glucose Tolerance Test, a random plasma glucose level of 200 mg/dL (11.1 mmol/L) or higher in individuals with symptoms of hyperglycemia and HbA1c of greater than 6.4%. HbA1c levels reflect different degrees of diabetes DM control.\u003csup\u003e12\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eLipid profile analysis is considered abnormal if TG level exceeded 150 mg/dL, total cholesterol \u0026gt; 200 mg/dl and low-density lipoprotein cholesterol (LDL-C) \u0026gt; 130 mg/dl. In addition to reduced HDL cholesterol level being less than 40 mg/dL for men and less than 50 mg/dL for women.\u003csup\u003e13\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDiagnostic Criteria for Metabolic Syndrome (METS)\u0026nbsp;\u003c/strong\u003eis identified when a person has at least three of the following five criteria: Abdominal Obesity, Elevated Triglycerides, Low HDL Cholesterol, Hypertension, and elevated Glucose level.\u003csup\u003e13\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMR Imaging Acquisition:\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMachine:\u003c/strong\u003e Abdominal MRI exams were performed on a 3 T Achieva; Philips medical system with body coil. Scan Time: 5 - 10 minutes. Subjects’ preparation: Fasting for 4-6 hours as it reduces bowel motion and gas, which can interfere with image quality. \u003cstrong\u003eFor MRI sequences\u003c/strong\u003e: Axial and coronal T2 WI for rapid acquisition images with an anatomical overview. T2* WI to estimate liver iron concentration. A cut off value \u0026lt; 2mg/g is considered normal. The MR-Dixon technique applied for fat quantification is a \u003cstrong\u003enon-contrast imaging method,\u0026nbsp;\u003c/strong\u003esix echo times were collected and R2* and fat-fraction images were generated automatically on the scanner. The acquisition was performed in a single breath-hold of 17 s, to reduce the motion artifacts, during the data acquisition. Table (1) shows the sequence acquisition parameters.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImage analysis:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMeasurement of Fat Fractions (FF)\u003c/strong\u003e: Manually selecting a specific region of interest (ROIs) in the target organs, the numerical values of the signal intensity (SI) on the generated fat and water images were obtained. Based on the acquired signal intensity values, the content of FF in individual organs was calculated.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo measure pancreatic fat and water signal three ROIs were placed in the head, body, and tail of the pancreatic parenchyma avoiding extra-pancreatic adipose tissue. Pancreatic duct and vessels were not included in the measurement, the average value of which was considered as the FF of whole pancreas. For liver, eight ROIs were depicted in each of segment, by avoiding bile ducts, large vessels, lesions, the border of the liver and image artifacts.\u003c/p\u003e\n\u003cp\u003eMeasurement of Abdominal Fat Area through the abdominal subcutaneous adipose area (SAT) and the visceral adipose tissue area (VAT) using the FF images. VAT was defined as intra-abdominal fat (including intraperitoneal and retroperitoneal fat) bound by parietal peritoneum or transversalis fascia, excluding the vertebral column and the paraspinal muscles. SAT was defined as fat superficial to the abdominal and back muscles. 3-point Dixon acquisition was prescribed at the level of the L3-L4 intervertebral space to estimate subcutaneous and visceral fat areas in this slice. Segmentation was done on Digital Imaging and Communications in Medicine (DICOM) images following anonymization. MRI of all included patients will be segmented on CoreSlicer platform, accessed via https://coreslicer.com/. It measures subcutaneous fat area and visceral fat area using an artificial intelligence model that performs a ROI analysis based on intensity range. The visceral fat index (VFI = amount of visceral fat/amount of visceral and subcutaneous fat) was calculated.\u003c/p\u003e\n\u003cp\u003eTwo additional ROIs were drawn on the paraspinal muscles bilaterally at the level of the third lumbar vertebra. The average FF for muscles was the arithmetic mean of the FF in the right and left paraspinal muscle.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatients:\u003c/strong\u003e Cases were initially categorized into two groups, T2DM and healthy controls. Pancreatic, hepatic, visceral and skeletal muscle (bilateral paraspinal muscles at lumbar 3 vertebra level) FF values were compared in both groups to find any significant differences. After that the FF values of the cases were compared to each other to detect any significant differences between their values correlating to their disease duration, age, body mass index and degree of disease control. Cases’ laboratory findings were collected to track disease progression and diabetic control, and correlated with FF to evaluate the m-Dixon MRI technique's ability to monitor these factors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e The collected data was revised, coded, tabulated, and introduced to Statistical package for Social Science (SPSS 21). Data was presented and suitable analysis was done according to the type of data obtained for each parameter. \u003cstrong\u003eDescriptive statistics:\u003c/strong\u003e Mean and Standard deviation for numerical data. Frequency and percentage of non-numerical data. \u003cstrong\u003eAnalytical statistics:\u003c/strong\u003e Student-t test was used to assess the statistical significance of the difference between two study group means. Chi-Square test was used to examine the relationship between two qualitative variables. Correlation analysis (using Spearman’s rho) was used to assess the strength of association between two quantitative variables. Mann Whitney Test (U test) was used to assess the statistical significance of the difference of a non-parametric variable between two study groups. The correlation coefficient denoted symbolically \"r\" defines the strength (magnitude) and direction (positive or negative) of the linear relationship between two variables. P value: level of significance P \u0026gt; 0.05: Non-significant (NS). P \u0026lt; 0.05: Significant (S).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThis study analyzed 30 cases, including 15 diabetic patients and 15 healthy controls. The mean age of participants was 40.27 years, with a mean BMI of 28.17 kg/m\u0026sup2;, classifying them in the normal and overweight/ obese categories according to WHO standards. The gender distribution showed a higher proportion of males (63.3%) compared to females (36.7%). METS was present in 53.3% of participants.\u003c/p\u003e\n\u003cp\u003eThe analysis examined the relationship between DM and several demographic and health factors, including gender, smoking status, METS, hypertension, and Central Obesity. (Table 2)\u003c/p\u003e\n\u003cp\u003eThe study compared body composition and biochemical parameters between diabetic and non-diabetic (NDM) groups. The diabetic group had a higher mean BMI compared to the NDM group, yet not statistically significant (p = 0.145). Hemoglobin A1c (HbA1c) levels were significantly higher in the diabetic group indicating poor glycemic control among diabetic individuals (p = 0.000). Lipid profiles showed no significant differences between both groups. However, FF measurements showed significant differences. (Table 3)\u003c/p\u003e\n\u003cp\u003eIn the assessment of body composition parameters and T2DM, PFF was the only parameter showing a significant association with T2DM risk, whereas BMI, HFF, and VFI did not exhibit statistically significant effects Table (4).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePancreatic Fat Fraction (PFF):\u003c/strong\u003e The AUC was 0.960 with a p-value \u0026lt; 0.001. The optimal cutoff for PFF was 6.35, showing 93% sensitivity and 93% specificity. Its high AUC indicated that it was very effective at distinguishing T2DM from controls due to its strong association with pancreatic dysfunction in diabetes. \u003cstrong\u003eHepatic Fat Fraction (HFF):\u003c/strong\u003e The AUC was 0.744 with a p-value of 0.023. The cutoff for HFF was 5.3, with 60% sensitivity and 80% specificity. HFF reflected fat accumulation in the liver, which was a known risk factor for insulin resistance and T2DM. While HFF was moderately effective, it was less precise than PFF. \u003cstrong\u003eVisceral Fat Index (VFI):\u003c/strong\u003e The AUC was 0.867 with a p-value of 0.001. The cutoff was 0.31, yielding 80% sensitivity and 93.3% specificity. VFI quantifies fat stored in the abdominal cavity, which was strongly associated with METS and T2DM. Its high AUC suggested it is a strong marker for T2DM due to its direct link with visceral fat\u0026apos;s role in insulin resistance. \u003cstrong\u003eBody Mass Index (BMI):\u003c/strong\u003e The AUC was 0.653 with a p-value of 0.152. The cutoff is 28.54, with 66.7% sensitivity and specificity. Its lower AUC indicated it was less effective than PFF and VFI in predicting T2DM. Overall, PFF and VFI were the most reliable parameters for distinguishing diabetics from controls due to their strong associations with diabetes-related metabolic changes, while HFF had moderate effectiveness and BMI was less predictive in this context Table (5) and Figure (1).\u003c/p\u003e\n\u003cp\u003eThere was a strong negative correlation between glycemic control and HFF, and a medium negative correlation with VFI. In contrast, there was a negative correlation with PFF. The analysis showed a weak negative correlation between BMI and glycemic control. Similarly, there was a negligible correlation with disease duration, with all parameters. Table (6).\u003c/p\u003e\n\u003cp\u003eRegarding body fat composition parameters, there was no statistically significant differences for each parameter (VFI, HFF, PFF, and BMI), between the insulin and oral medication groups, suggesting similar body fat composition profiles across treatment types Table (7).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eRegarding diabetic and control groups, the study's sample demonstrated an equal distribution of T2DM across genders, suggesting balanced representation between diabetic and non-diabetic individuals. T2DM showed notable associations with age, hypertension, central obesity, and METS. However, BMI, Smoking and Gender did not exhibit a significant correlation. Also, we observed no correlation between serum triglycerides, total cholesterol, LDL, HDL, and total adipose tissue distribution quantified by MRI in the T2DM patients, consistent with findings by \u003cstrong\u003eSarma et al.\u0026nbsp;\u003c/strong\u003e\u003csup\u003e14\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLipid profile findings analysis:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBy analyzing Lipid profile findings and different body composition parameters for the participants,\u003c/strong\u003e Triglycerides exhibited strong positive correlations with PFF, VFI, HFF, and Paraspinal Fat Fraction (PSFF) (r = 0.541, r = 0.454, r = 0.483, and r = 0.459, respectively). Similarly, VLDL was significantly correlated with pancreatic fat. The TG/HDL ratio was also significantly associated with pancreatic, hepatic, visceral area fat deposition, reinforcing its role as a predictor of metabolic risk. These findings align with the results of \u003cstrong\u003eZhang et al.,\u003c/strong\u003e who reported a strong correlation between HFF and TG (r = 0.306), and a weaker correlation with PFF (r = 0.183), yet his results showed \u0026nbsp;no significantly correlated between intramuscular FF and TG. These findings suggest that hepatic fat may be involved in glycolipid regulation and could serve as a useful marker for identifying individuals at higher risk for cardiometabolic conditions.\u003csup\u003e15\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eDe novo adipogenesis is a state of chronic inflammation that increases adipocyte lipolysis, leading to ectopic fat accumulation in organs, after exceeding the capacity of SAT causing adipocytes hypertrophy.\u003csup\u003e16\u003c/sup\u003e The accumulation of fat in skeletal muscle, liver and pancreas may contribute to IR, inhibiting glucose uptake in muscle cells, increasing hepatic gluconeogenesis, and decreasing glycogen synthesis.\u003csup\u003e17\u003c/sup\u003e Our findings revealed significant differences in several body composition parameters including PFF, VFI, bone marrow adipose tissue (BMAT) between DM group (11.74%, 0.40, and 48.97%, respectively) compared to the NDM group (3.61%, 0.23%, and 32.75%, respectively). The HFF showed a higher mean in the DM group (8.51 ± 7.20) compared to the NDM group (4.47 ± 3.72), yet this difference did not reach statistical significance in this study (p = 0.064). In agreement with our findings, \u003cstrong\u003eAn et al.\u003c/strong\u003e reported higher values for VAT, HFF, PFF, and BM FF in the diabetic group (187.89 cm², 4.12%, 13.05%, and 46.56%, respectively) compared to the NDM group (139.95 cm², 3.40%, 6.70%, and 42.91%, respectively).\u003csup\u003e18\u003c/sup\u003e All these parameters showed statistical significance between the two groups.\u003csup\u003e18\u003c/sup\u003e Figures (2), (3) and (4).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePancreatic fat fraction (PFF):\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNonalcoholic fatty pancreatic disease (NAFPD) has been associated with IR, and inflammation, which may contribute to glucose metabolism disorders and diabetes.\u003csup\u003e19\u003c/sup\u003e Our findings are consistent with \u003cstrong\u003eAn et al,\u003c/strong\u003e which identified pancreatic fat as an independent risk factor for T2DM.\u003csup\u003e\u0026nbsp;18\u003c/sup\u003e Similarly, \u003cstrong\u003eChan et al\u003c/strong\u003efound that each percentage increase in pancreatic fat was linked to a 7% rise in the risk of developing diabetes (aHR 1.07; 95% CI, 1.00–1.16; P = 0.063).\u003csup\u003e20\u003c/sup\u003e In this study, PFF significantly increased the risk of T2DM, with an odds ratio of 2.201, suggesting that higher levels of pancreatic fat are strongly associated with increased diabetes risk.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAn et al\u003c/strong\u003e additionally reported that PFF ( AUC of 0.787, with a sensitivity and specificity of 75.00% and 77.29%) show higher predictive value than VAT (AUC of 0.685, with a sensitivity of 67.65% and specificity of 63.37%)in differentiating diabetic and non-diabetic individuals, suggesting that PFF is a stronger predictor of T2DM than VAT, which has a moderate ability to differentiate between groups.\u003csup\u003e18\u0026nbsp;\u003c/sup\u003eThis findings were further supported by \u0026nbsp;\u003cstrong\u003eTirkes et al,\u0026nbsp;\u003c/strong\u003eillustrating that PFF has a strong predictive value for identifying T2DM with an AUC of 0.85, having a sensitivity of 69% and specificity of 87%.\u003csup\u003e21\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, PFF demonstrated even greater discriminatory power, with an AUC of 0.960 and both sensitivity and specificity at 93% at a lower cutoff of 6.35%. This highlights PFF's strong predictive value for identifying T2DM and its superiority over VAT and other metrics.\u003csup\u003e22\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGlycemic Control:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe observed no significant difference between glycemic control and PFF in diabetic groups, however we found that glycemic control was strongly negatively correlated with HFF and moderately negatively correlated with VFI, suggesting that poor glycemic control is more strongly linked to increased liver and visceral fat rather than pancreatic fat. This could be attributed to the irreversible nature of fat deposition in the pancreas,\u003csup\u003e23\u003c/sup\u003e indicating that other factors besides glycemic control may contribute to the regression of pancreatic pathology. The pancreatic steatosis is a potentially reversible condition, significant reductions in pancreatic fat were observed with 8.9% decrease in body weight or a daily caloric intake of 600 kcal.\u003csup\u003e24,25\u003c/sup\u003e Other study conducted by \u003cstrong\u003eSalman et al,\u003c/strong\u003e reported that bariatric surgery resulted in notable reductions in BMI, HbA1c, fasting insulin, HOMA-IR, and total VAT volume, while also improving the lipid profile.\u003csup\u003e26,27\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDuration of Diabetes:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, there was no statistically significant differences in PFF based on disease duration. In contrast, \u003cstrong\u003eYi et al,\u0026nbsp;\u003c/strong\u003ereportedthatthere was a significant difference in PFF between patients with a disease duration of more than 5 years and those with a 1-5 years duration (P \u0026lt; 0.001), but not less than that, which could be attributed to a smaller sample size in this study.\u003csup\u003e28\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHepatic fat fraction (HFF) and Visceral fat index (VFI):\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study did not find a statistically significant impact of HFF on T2DM risk, aligning with \u003cstrong\u003eZheng et al;\u003c/strong\u003e\u003csup\u003e29\u003c/sup\u003e\u003cstrong\u003e\u0026nbsp;An et al\u003c/strong\u003e noted that while T2DM patients had higher HFF, hepatic fat deposition may not independently contribute to T2DM development, and its role could be influenced by visceral fat, which together affect T2DM risk.\u003csup\u003e18\u003c/sup\u003e Previous studies have shown that severe IR and T2DM risk are seen in individuals with hepatic steatosis resulting from unhealthy lifestyles and excess VAT accumulation.\u003csup\u003e31\u003c/sup\u003eContrary to our findings, \u003cstrong\u003eSarma et al.\u003c/strong\u003e found that increased fat deposition in the liver, pancreas, and viscera was related to IR in T2DM,\u003csup\u003e14\u003c/sup\u003e while \u003cstrong\u003eCao et al\u003c/strong\u003e observed that T2DM and prediabetic patients had higher HFF than those with normal glucose tolerance.\u003csup\u003e30\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eHFF and VFI also showed significant discriminatory ability between diabetic and control groups in our study, with AUCs of 0.744 and 0.867, respectively. \u0026nbsp;Yet, these values were slightly lower compared to PFF with AUC of 0.960. However, we suggest the predictive accuracy of HFF may be diminished in patients who are overweight or have METS due to overlapping results, as these conditions can influence fat deposition patterns, leading to decreased test accuracy. \u003cstrong\u003eDucluzeau et al\u003c/strong\u003e demonstrated that patients with T2DM and those with met METS exhibit similar degrees of hepatic steatosis, with HFF values of 16.8% and 15%, respectively, suggesting that liver fat alone may not be sufficient for distinguishing between these conditions.\u003csup\u003e31\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBody mass index (BMI):\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn our study, BMI was not significantly associated with T2DM risk, yet there was modest correlation with central obesity (r = 0.333, p \u0026gt; 0.05), suggesting that it may play more crucial roles in identifying T2DM risk. \u003cstrong\u003eLevelt et al\u003c/strong\u003e reported that T2DM is linked to increased hepatic triglyceride, regardless of obesity.\u003csup\u003e32\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAdditionally, our study found non-significant correlations between BMI and VFI, as well as BMI and PFF. This aligns with findings by \u003cstrong\u003eGaborit et al\u003c/strong\u003e, who reported that pancreatic fat is independent of BMI.\u003csup\u003e33\u003c/sup\u003e However, we did observe a moderate positive correlation between BMI and HFF, indicating that higher BMI is linked to greater liver fat, consistent with findings by \u003cstrong\u003eChen et al,\u003c/strong\u003e who also noted positive associations between HFF, BMI, and triglycerides.\u003csup\u003e34\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eMoreover, our results revealed strong relationships between HFF and PFF, with VFI. In comparison, \u003cstrong\u003eWaddell et al\u003c/strong\u003e found weak associations between VFI and pancreas fat. This suggests that visceral fat is a key driver of ectopic fat deposition in both the liver and pancreas.\u003csup\u003e35\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eThese findings highlight the importance of reducing visceral and hepatic fat for optimal diabetes management. Effective glycemic plays a key role in mitigating ectopic fat, including fat accumulation in the liver and visceral areas.\u003csup\u003e36\u003c/sup\u003e The strong associations between visceral fat, liver fat, and pancreatic fat emphasize the need for focused strategies to manage these fat compartments in T2DM care.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDiabetic medications effect:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, there were no statistically significant differences in body fat composition parameters between patients treated with subcutaneous insulin injection and those receiving oral hypoglycemic medications, suggesting that both types of treatment groups had similar body fat profiles.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn a randomized trial by \u003cstrong\u003eGastaldelli et al\u003c/strong\u003e, diabetic patients treated with tirzepatide for 52 weeks showed a significant reduction in both liver fat content and VAT volumes compared to those treated with insulin degludec. These findings suggest that liver fat fraction and VAT, could be valuable in future studies assessing the development, progression, and treatment response of liver fat burden in patients with T2DM during larger clinical trials.\u003csup\u003e37\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy limitations and recommendations:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study faced several limitations, including a small sample size of 30 participants, split into 15 diabetic and 15 non-diabetic individuals, which limited the statistical power of the findings. The small sample size likely contributed to wide confidence intervals, particularly for the VFI. Additionally, the cross-sectional design of the study made it challenging to establish causality between diabetes and body composition parameters. Furthermore, the study relied on single-time measurements of body fat and glycemic control, meaning any changes over time were not considered.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFuture research should aim to include larger and more diverse samples to enhance the generalizability of the findings. Longitudinal study designs would allow for the examination of changes over time in diabetes status, body fat composition, and glycemic control, providing a better understanding of the causal relationships.\u0026nbsp;\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study found that Pancreatic, hepatic, and visceral fat levels were significantly higher in diabetics, especially in those with poor glycemic control. Pancreatic fat emerged as a strong indicator of diabetes risk. BMI showed weaker associations with diabetes, and disease duration or treatment type did not impact body fat composition. Further research is needed to confirm these findings and their role in managing T2DM. Quantification of ectopic fat fractions using the MR-Dixon technique offers a non-invasive, accurate, and clinically valuable approach to assess metabolic risk in T2DM. It enhances understanding of disease pathophysiology, aids early detection of complications, and provides a reliable tool for monitoring therapeutic response\u0026mdash;thereby supporting precision medicine in the management of Type 2 Diabetes Mellitus.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003eADA\u003c/strong\u003e:\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eAmerican Diabetes Association\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAHA\u003c/strong\u003e: American Heart Association\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAT:\u003c/strong\u003e Adipose Tissue\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAUC\u003c/strong\u003e: Area under the Curve\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBMAT\u003c/strong\u003e: Bone Marrow Adipose Tissue\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBMI\u003c/strong\u003e: Body Mass Index\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCI\u003c/strong\u003e: Confidence Interval\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDICOM\u003c/strong\u003e: Digital Imaging and Communications in Medicine\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDM\u003c/strong\u003e: Diabetes Mellitus\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFA\u003c/strong\u003e: Fatty Acid\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFF\u003c/strong\u003e: Fat Fraction\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFFA\u003c/strong\u003e: Free Fatty Acids\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHbA1c\u003c/strong\u003e: Hemoglobin A1c\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHFF\u003c/strong\u003e: Hepatic Fat Fraction\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIDF\u003c/strong\u003e: International Diabetes Federation\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIQR\u003c/strong\u003e: Interquartile Range\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIR\u003c/strong\u003e: Insulin Resistance\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIVC\u003c/strong\u003e: Inferior Vena Cava\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLDL-C\u003c/strong\u003e: Low-Density Lipoprotein Cholesterol\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMetS\u003c/strong\u003e: Metabolic Syndrome\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMR\u003c/strong\u003e: Magnetic Resonance\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMRI-PDFF\u003c/strong\u003e: MRI-Proton Density Fat Fraction\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNAFLD\u003c/strong\u003e: Non-Alcoholic Fatty Liver Disease\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNAFPD\u003c/strong\u003e: Nonalcoholic Fatty Pancreatic Disease\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNASH\u003c/strong\u003e: Non-Alcoholic Steatohepatitis\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNDM\u003c/strong\u003e: Non-Diabetic\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNHLBI\u003c/strong\u003e: National Heart, Lung, and Blood Institute\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNS\u003c/strong\u003e: Non-Significant\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOGTT\u003c/strong\u003e: Oral Glucose Tolerance Test\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOR\u003c/strong\u003e: Odds Ratio\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePDFF\u003c/strong\u003e: Proton Density Fat Fraction\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePFF\u003c/strong\u003e: Pancreatic Fat Fraction\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePI\u003c/strong\u003e: Pancreatic Index\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePSFF\u003c/strong\u003e: Paraspinal Fat Fraction\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePUFAs\u003c/strong\u003e: Polyunsaturated Fatty Acids\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eROC\u003c/strong\u003e: Receiver Operating Characteristic\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eROI\u003c/strong\u003e: Region-of-Interest\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSAT\u003c/strong\u003e: Subcutaneous adipose tissue\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSI\u003c/strong\u003e: Signal Intensity\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eT2DM\u003c/strong\u003e: Type 2 Diabetes Mellitus\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTE\u003c/strong\u003e: Time Echo\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTG\u003c/strong\u003e: Triglycerides\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVAT\u003c/strong\u003e: Visceral adipose tissue\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVFI\u003c/strong\u003e: Visceral Fat Index\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVLDL\u003c/strong\u003e: Very Low-Density Lipoprotein\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVOI\u003c/strong\u003e: Voxel of Interest\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWC\u003c/strong\u003e: Waist Circumference\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWHO\u003c/strong\u003e: World Health Organization\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003e\u003cstrong\u003eNCD Risk Factor Collaboration (NCD-RisC). 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(2023). \u003c/strong\u003eA prediction model of liver fat fraction and presence of non-alcoholic fatty liver disease (NAFLD) among patients with overweight or obesity. Endocrine journal, 70(10), 977\u0026ndash;985.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eWaddell, T., Bagur, A., Cunha, D., Thomaides-Brears, H., Banerjee, R.,Cuthbertson, D. J., Brown, E., Cusi, K., Despr\u0026eacute;s, J. P., \u0026amp;amp; Brady, M. (2022). \u003c/strong\u003eGreater ectopic fat deposition and liver fibroinflammation and lower skeletal muscle mass in people with type 2 diabetes. Obesity (Silver Spring, Md.), 30(6),1231\u0026ndash;1238.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eKazeminasab, F., Bahrami Kerchi, A., Behzadnejad, N., Belyani, S., Rosenkranz, S. K., Bagheri, R., \u0026amp; Dutheil, F. (2024). \u003c/strong\u003eThe Effects of Exercise Interventions on Ectopic and Subcutaneous Fat in Patients with Type 2 Diabetes Mellitus: A Systematic Review, Meta-Analysis, and Meta-Regression. Journal of clinical medicine, 13(17), 5005.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eGastaldelli, A., Cusi, K., Fern\u0026aacute;ndez Land\u0026oacute;, L., Bray, R., Brouwers, B., \u0026amp; Rodr\u0026iacute;guez, \u0026Aacute;. (2022). \u003c/strong\u003eEffect of tirzepatide versus insulin degludec on liver fat content and abdominal adipose tissue in people with type 2 diabetes (SURPASS-3 MRI): a substudy of the randomised, open-label, parallel-group, phase 3 SURPASS-3 trial. The lancet. Diabetes \u0026amp; endocrinology, 10(6), 393\u0026ndash;406.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 to 7 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Type 2 Diabetes Mellitus (T2DM), Ectopic Fat, MRI Dixon, Glycemic Control","lastPublishedDoi":"10.21203/rs.3.rs-5656230/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5656230/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eType 2 diabetes mellitus (T2DM), a condition affecting over 366 million individuals by 2030, is intimately associated with obesity, insulin resistance, and the accumulation of ectopic fat, particularly in the liver and pancreas. MRI Dixon, a superior imaging technique, offers enhanced assessment of pancreatic fat compared to traditional ultrasound methods. This study aimed to evaluate ectopic fat accumulation in the pancreas, liver, and paraspinal skeletal muscles in T2DM patients compared to healthy controls using MRI Dixon technique.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eUsing a 3T MRI with the m-Dixon sequence, the liver and pancreas of 15 T2DM patients and 15 healthy volunteers. We measured pancreatic fat fraction (PFF), hepatic fat fraction (HFF), body mass index (BMI), and fat fractions in visceral adipose tissue (VAT), and subcutaneous adipose tissue (SAT). We compared these parameters between the experimental and control groups and analyzed correlations between PFF and other indicators.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eDiabetic participants exhibited a significantly higher PFF (11.74 ± 3.46) compared to controls (3.61 ± 2.60; p = 0.000). Other fat measurements, including SAT, visceral fat index (VFI), psoas muscle fat fraction (PS FF), and bone marrow fat fraction (BM FF), were also elevated in diabetics (p \u0026lt; 0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003ePancreatic fat is a crucial indicator of T2DM, showing superior predictive performance relative to other fat measures. Higher levels of liver and visceral fat correlate with poor glycemic control, underscoring the importance of managing blood glucose levels.\u003c/p\u003e","manuscriptTitle":"Quantification of Ectopic Fat Fractions in Type 2 Diabetes Mellitus using MR-Dixon Technique","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-06 12:25:20","doi":"10.21203/rs.3.rs-5656230/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7fa70736-5ba7-4b39-8718-b5eb1ea7167e","owner":[],"postedDate":"May 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-07-14T16:08:23+00:00","versionOfRecord":{"articleIdentity":"rs-5656230","link":"https://doi.org/10.1186/s43055-025-01500-6","journal":{"identity":"egyptian-journal-of-radiology-and-nuclear-medicine","isVorOnly":false,"title":"Egyptian Journal of Radiology and Nuclear Medicine"},"publishedOn":"2025-07-09 15:57:29","publishedOnDateReadable":"July 9th, 2025"},"versionCreatedAt":"2025-05-06 12:25:20","video":"","vorDoi":"10.1186/s43055-025-01500-6","vorDoiUrl":"https://doi.org/10.1186/s43055-025-01500-6","workflowStages":[]},"version":"v1","identity":"rs-5656230","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5656230","identity":"rs-5656230","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}