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Association of dietary glycemic index and glycemic load with pancreatic steatosis: a case control study

BMC Endocrine Disorders volume 25, Article number: 89 (2025) Cite this article

Abstract

Background

Carbohydrate intake, its type and characteristics including glycemic index (GI) and glycemic load (GL) may be associated with the risk of pancreatic steatosis (PS), but there is no conclusive evidence. The aim of the present study was to investigate whether the intake of carbohydrates, GI and GL were associated with an increased risk of PS.

Methods

To conduct this study, 278 patients with common bile duct stones (CBD) underwent endoscopic ultrasound, including 89 patients with PS (case group) and 189 healthy individuals (control group). In addition to demographic and anthropometric information, a 168-item questionnaire of food frequency was completed to calculate GL and GI.

Results

With the increase of GI and GL, the number of patients with PS increased significantly (P = 0.013, P < 0.001, respectively) and the risk of PS increased significantly. A similar increase in risk of PS was found with increased risk of carbohydrate, simple sugar and fructose intake. After adjusting all the confounders, the risk of PS with increasing simple sugar and fructose intake was 4.3 times (OR T3 vs. T1 = 4.3, 95% CI: 1.7–10.6, P trend < 0.001) and 5.3 times (OR T3 vs. T1 = 5.3, 95% CI: 2.2–12.9, P trend < 0.001), respectively, compared to the first tertile. Conversely, increased fiber intake showed a reverse association with the PS, so that those in the second and third tertiles of fiber intake were 84% (OR = 0.16, 95% CI: 0.05–0.45) and 87% (OR = 0.13, 95% CI: 0.04–0.39) less at risk of developing PS, respectively (P trend = 0.001).

Conclusions

These findings support the hypothesis of direct associations between GI and GL increased risk of PS.

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Introduction

In 1933, the concept of pancreatic steatosis (PS) was introduced by Ogilvie, who observed that obese individuals have higher fat levels in the pancreas than non-obese individuals [1]. PS is a broad term that refers to the accumulation of fat in the pancreas and, when triggered by obesity, is known as non-alcoholic fatty pancreas disease (NAFPD) [2]. Extensive investigations in Asian populations have reported PS prevalence rates ranging from 16 to 35% [3], and a meta-analysis based on pooled data found an overall prevalence of 33% for NAFPD [4]. Moreover, research has yielded substantial evidence that establishes a direct correlation between pancreatic cancer and PS [5, 6]. Pancreatic cancer is the seventh most prevalent cause of cancer-related deaths worldwide and is responsible for over 331,000 deaths annually [7]. Previous studies have also demonstrated the association of PS with metabolic syndrome, type 2 diabetes mellitus, hypertension [8, 9], and obesity [8,9,10]. Nevertheless, there is currently no specific treatment for PS, and its management is primarily focused on addressing its underlying causes [11]. Diet has emerged as a crucial factor in the development of PS and its role seems to be promising in PS management [12].

The glycemic index (GI) and glycemic load (GL) of foods are two of the quality measures for carbohydrates that predict the glycemic response after meals. It is widely accepted that the quality and quantity of carbohydrates are the main factors that determine the glycemic response and the release of insulin after a meal [13, 14].

Current literature suggests that a low-glycemic index diet has potential benefits in reducing body weight, total body fat and visceral fat, levels of pro inflammatory markers and the occurrence of dyslipidemia and hypertension [15], all of which could influence the pathophysiology of PS [12]. Moreover, the advantageous impacts of low GI and GL have been examined in relation to similar conditions, such as fatty liver and it was determined that diets with low GI and GL may decrease the amount of fat in the liver [16].

Considering the relatively high occurrence of PS, the absence of targeted treatment methods, and its connection to pancreatic cancer, effectively managing PS is crucial. The primary objective of the present study is to provide further insights and improve the existing body of literature on dietary factors that are linked to PS. Specifically, our focus is to investigate the correlation between the quality and quantity of carbohydrates in one’s diet and the risk of PS.

Material & methods

Study design and ethics considerations

The present study was designed as a case-control with 278 participants. After the approval of the Research Ethics Committee of Shahid Beheshti University of Medical Sciences (IR.SBMU.NNFTRI.REC.1402.689), sampling started in 2022 by consecutive-sampling method. Sampling, endoscopic sonography and other investigations were performed in the gastroenterology clinic of Ayatollah Taleghani Hospital, Tehran, Iran. Before commencement, the study protocol and objectives were explained to the patients. Each participant was assigned a code, while face-to-face interviews and measurements were conducted in a private room, and participants were assured of confidentiality. This study was conducted in accordance with the ethical guidelines of the Helsinki Declaration. All participants signed a written informed consent form.

Participants

The participants of the present study were selected from patients with common bile duct stones who underwent endoscopic ultrasound (EUS). Based on the diagnosis of a skilled gastroenterologist and hepatologist and according to relevant guidelines [17], 89 patients were diagnosed with PS and the other 189 patients were healthy in this respect. Conscious and interested adults over 18 years of age were included in the study. The study exclusion criteria can be mentioned as follows: Pregnancy or breastfeeding, active malignancy and severe concomitant diseases including hepatitis, cirrhosis and kidney failure.

Data collection

First, demographic and lifestyle information was completed using a questionnaire. In order to avoid random observer error, a skilled nutritionist with more than 5 years of research experience performed all anthropometric measurements for cases and control groups. Body weight was assessed using a digital scale (Seca, Germany) with an accuracy of ± 0.1 kg. For anthropometric measurements, participants were instructed to wear light clothing without shoes or hats. Participants’ height was measured in a standing position with a wall-mounted stadiometer (Seca, Germany) and rounded to the nearest 0.5 cm. The body mass index (BMI) was computed by dividing the weight (kg) by the square of height (m2).

Calculation of dietary intakes, GI and GL

Food consumption during the past year (before the diagnosis of PS for cases and before the interview in controls) was estimated using a reliable and valid semi-quantitative food frequency questionnaire (FFQ). The mentioned questionnaire was designed based on Willett’s method [18] and its validity and reliability [19] have already been measured. After explaining the household measures, the participants were asked about the frequency of consumption of each food item. Then, using the USDA food composition table along with the Iranian food composition table, the average daily intake of energy and macronutrients was evaluated for each participant.

GI values ​​were estimated using the international table of GI and GL values [20]. For items not on these lists, GI values ​​were estimated based on foods with similar nutritional composition or calculated using related formula [21]. To calculate the dietary GI of each participant, we multiplied the carbohydrate content of each food items by its GI and the frequency of consumption and divide the result by the total carbohydrate intake. Then GIs of individual food items were summed up. The GI for whole and refined grain, vegetables, fruits, dairy products, and seeds and nuts was obtained from the international table of GI [22], the GI online database of the University of Sydney [23], and the publication that lists the GI of Iranian foods [24]. To calculate GL, GI in total available carbohydrate was multiplied and divided by 100. Each unit of dietary GL represents the equivalent of 1 g carbohydrate from glucose.

Dietary GI = [(carbohydrate content of each food item) × (number of servings/d) × (GI)]/total daily carbohydrate intake.

Dietary GL= (carbohydrate content of each food item) × (number of servings/d) × (GI).

Statistical analysis

Statistical data analysis was conducted using SPSS version 21.0 (SPSS, Inc.). All hypothesis tests were 2-tailed, with P values < 0.05 considered statistically significant. Data were tertiled by GL and GI to best fit the data distribution, simplify interpretation, and perform a comparison between triplicates without extreme differences in sample size between groups. Quantitative variables were compared between GI and GL tertiles, as well as between case and control groups, respectively, using ANOVA and independent t-test, and the results were reported as mean ± standard deviation (SD). Chi-square test was used to compare quantitative variables and the values ​​were reported as frequency and percentage. The association of GI, GL and different types of carbohydrate with PS was assessed using binary logistic regression with odds ratios (ORs) and 95% confidence intervals (CIs). Age, sex, BMI and energy intake were considered as confounding factors. Three statistical models were defined. In the first model, the results were reported without considering confounders (crude). The second model was adjusted for age and sex, and the third model was additionally adjusted for BMI and energy intake. In all analyses, the first tertile was considered as the reference.

Results

Table 1 shows the characteristics of the participants at the beginning of the study. With the increase of glycemic index and glycemic load, the number of patients with PS increased significantly (P = 0.013, P < 0.001, respectively). A total of 94 men and 184 women participated in the study. The comparison of the gender across the GI and GL tertiles showed that the quantity of men increased with the increase in GI and GL, although this increase was statistically significant only in the GL tertiles (P = 0.011). No difference was observed in terms of age, smoking (except for GI, P = 0.021) and alcohol consumption. Differences in anthropometric characteristics including height, weight and BMI also showed statistical differences between GI and GL tertiles, except for BMI in GL.

Table 1 Baseline general characteristics of study participants
Full size table

Table 2 shows the difference in dietary intakes of case and control groups. As shown, although the two groups do not differ significantly in terms of calorie intake, the intake of carbohydrates, simple sugar, and fructose in the patients is higher than the control group, and the intake of fiber is lower. Therefore, the glycemic index and glycemic load in the case group were estimated higher than the control group. The minimum and maximum GI values ​​were 45.3 and 82.52, respectively, and the minimum and maximum GL values ​​were 53.25 and 333.52, respectively.

Table 2 Mean ± Standard deviation of dietary factors among cases with pancreatic steatosis and matched controls
Full size table

Table 3 describes the risk of pancreatic steatosis according to dietary intakes of some types of carbohydrates and GI and GL. With the increase of GI and GL, the risk of PS increased significantly. This increased risk was reinforced by adjusting the results for confounding factors. Also, in the pairwise tertile comparisons, it was found that those in the third tertile of GI and GL were significantly more at risk of PS compared to the reference group. Although this comparison was not significant between the second tertile and the reference group except for GL in the model 3 (OR = 2.1, 95%CI: 1.1–3.9). With the increase in carbohydrate intake, the number of patients and the risk of PS increased significantly. However, by adjusting the results for confounding factors, this increase in risk became a little weaker, so that in model 3, after adjusting the effect of all confounders, P was close to the significant level (P = 0.049).

Table 3 Odds and 95% confidence interval for occurrence of the pancreatic steatosis
Full size table

A similar increase in risk of PS was found with increased risk of simple sugar and fructose intake. In model 3, after adjusting all the confounders, the risk of PS with increasing simple sugar and fructose intake was 4.3 times (OR T3 vs. T1 = 4.3, 95% CI: 1.7–10.6, P trend < 0.001) and 5.3 times (OR T3 vs. T1 = 5.3, 95% CI: 2.2–12.9, P trend < 0.001), respectively, compared to the first tertile. Conversely, increased fiber intake showed a reverse association with the PS. Logistic regression results after adjusting for all confounders indicated that those in the second and third tertiles of fiber intake were 84% (OR = 0.16, 95% CI: 0.05–0.45) and 87% (OR = 0.13, 95% CI: 0.04–0.39) less at risk of developing PS, respectively (P trend = 0.001).

Discussion

To the best of our knowledge, the present study is the first to provide valuable insights into the association between the quality and quantity of carbohydrates and the risk of PS. Our results suggested that diets with a high GI, GL, carbohydrate, simple sugar, and fructose content may increase the risk of PS. Specifically, being in the last tertiles of GI and GL was associated with a 2.5-fold and 3.5-fold higher risk of PS. Likewise, our findings indicated that higher consumption of carbohydrate, simple sugar, and fructose is associated with a 4.3-fold, 2-fold, and 5.3-fold higher risk of PS, respectively. Conversely, our findings indicated that higher fiber intake appears to have a reverse association with the PS, possibly due to its role in improving insulin sensitivity [25, 26] and reducing inflammation [27, 28]. Remarkably, individuals with higher fiber consumption demonstrate an 87% lower risk of developing PS.

Although the relationship between high glycemic indices, carbohydrates, and PS is not well established, there are some potential connections to consider that can explain our findings. Multiple investigations have demonstrated a clear correlation between obesity, elevated visceral adipose tissue, and pancreatic fat accumulation [9, 29]. Obesity is a triggering factor for insulin resistance [30, 31] and the current evidence suggests that there is an association between insulin resistance and PS. For instance, a study by Weng et al. [32] demonstrated that the homeostatic model assessment of insulin resistance (HOMA-IR) is an independent risk factor for NAFPD. Similarly, another study by van der Zijl et al. [33], which involved patients with impaired glucose tolerance, found an inverse correlation between pancreatic fat content and insulin sensitivity. Furthermore, Lee et al. [34] discovered that HOMA-IR tended to increase with the severity of NAFPD. Specifically, in multivariate logistic regression analysis, HOMA-IR was correlated with NAFPD after adjusting for age, BMI, and lipid profiles. Nonetheless, the significant correlation between NAFPD and HOMA-IR disappeared when further adjustments were made for visceral adipose, indicating that visceral adipose may have a more prominent role in either contributing to or mediating the connection between NAFPD and insulin resistance. Dysfunctional adipose tissues in obese individuals contribute to early-stage insulin resistance through the excessive release of free fatty acids (FFAs), reactive oxygen species (ROS), and pro-inflammatory cytokines [30]. This elevation in FFAs produces toxic lipids, such as ceramide, that disrupt cellular organelles, including mitochondria, endoplasmic reticulum, and lysosomes [30, 35]. The malfunction of these organelles leads to apoptosis, systemic dysfunction, and cellular impairment, which in turn increase the release of FFAs and pro-inflammatory substances [30]. This condition eventually exacerbates insulin resistance, which raises the levels of FFAs in the body and encourages the build-up of fat in organs such as the pancreas [12, 30]. Interestingly, diets high in free sugar and correspondingly high glycemic indexes have been linked to obesity and the development of insulin resistance [36, 37].

The other potential explanation for our findings is related to inflammation. In a study in which 30 obese and 30 lean female mice were compared, the obese mice exhibited a higher fat content, triglycerides, free fatty acids, cholesterol, and pro-inflammatory cytokines (IL-1β and TNF-α) in their pancreas [38]. Research has demonstrated that obesity induces chronic low-grade inflammation, which in turn results in an increase in pro-inflammatory cytokines, including interleukin-1β (IL-1β) and tumor necrosis factor-alpha (TNF-α) [12]. In addition, obesity disrupts the delicate equilibrium of cytokines by decreasing the production of the anti-inflammatory cytokine IL-10 in the spleen [12, 39]. Notably, diets that have high glycemic indexes have been linked to causing chronic, low-grade inflammation in the body, both directly and indirectly. The consumption of high-glycemic index foods leads to excessive postprandial blood glucose excursions, which in turn generate nitric oxide, which, in combination with superoxide, produces peroxynitrite ( a potent and long-lasting pro-oxidant molecule) [40]. Therefore, the consumption of foods with a high glycemic index can induce oxidative stress and chronic low-grade inflammation [41]. Overall, these inflammatory responses (due to either obesity or diet) lead to elevated levels of triglycerides, FFAs, cholesterol, and fat accumulation in the pancreas [12]. Additionally, a study conducted by DiNicolantonio et al. [42] highlighted that inflammation triggered by fructose intake leads to an increase in intracellular cortisol, which, in turn, contributes to the development of visceral adiposity. This means that fat cells release fatty acids into visceral organs such as the liver and pancreas, disrupting metabolic processes and organ function.

The current study possesses several noteworthy strengths. First, the present study provides the first evidence of an association between the quality and quantity of carbohydrates and PS odds. Second, a validated FFQ was used for dietary data collection by an expert dietitian who was unaware of the diagnosis. Third, a specialist performed the diagnosis, and it was similar for both groups to control information bias. Nonetheless, there are several limitations to this study. Selection bias, measurement bias, and recall bias for FFQ may lead to misleading findings in a case-control study. Additionally, while we examined adjusted models to account for potential confounding factors, it was not possible to assess genetic factors and other potential factors so it is crucial to recognize the possibility of undiscovered confounding factors. Physical activity levels and inflammatory biomarkers, which can influence insulin resistance and fat deposition, were also not taken into account. Finally, the study design restricts the establishment of a causal relationship, and the generalizability of the findings may be restricted to the specific population under investigation.

Conclusion

In conclusion, this case-control study highlights that diets high in GI, GL, carbohydrates, simple sugar, and fructose may increase the risk of PS, while higher fiber intake is associated with a lower risk of PS. Nevertheless, further prospective studies are warranted to confirm these associations and explore the underlying mechanisms in more detail.

Data availability

The datasets analyzed in the current study are available from the corresponding author on reasonable request.

References

  1. Schaefer JH, THE NORMAL WEIGHT OF THE PANCREAS IN. THE ADULT HUMAN BEING: A BIOMETRIC STUDY ¹. Anat Rec. 1926;32(2):119.

    Google Scholar 

  2. Smits MM, van Geenen EJ. The clinical significance of pancreatic steatosis. Nat Rev Gastroenterol Hepatol. 2011;8(3):169–77.

    PubMed  Google Scholar 

  3. Yu TY, Wang CY. Impact of non-alcoholic fatty pancreas disease on glucose metabolism. J Diabetes Invest. 2017;8(6):735–47.

    Google Scholar 

  4. Singh RG, Yoon HD, Wu LM, Lu J, Plank LD, Petrov MS. Ectopic fat accumulation in the pancreas and its clinical relevance: a systematic review, meta-analysis, and meta-regression. Metabolism. 2017;69:1–13.

    PubMed  CAS  Google Scholar 

  5. Rebours V, Gaujoux S, d’Assignies G, Sauvanet A, Ruszniewski P, Lévy P, et al. Obesity and fatty pancreatic infiltration are risk factors for pancreatic precancerous lesions (PanIN). Clin Cancer Res. 2015;21(15):3522–8.

    PubMed  CAS  Google Scholar 

  6. Hori M, Takahashi M, Hiraoka N, Yamaji T, Mutoh M, Ishigamori R, et al. Association of pancreatic fatty infiltration with pancreatic ductal adenocarcinoma. Clin Translational Gastroenterol. 2014;5(3):e53.

    CAS  Google Scholar 

  7. Truong E, Pandol S, Jeon C. Uniting epidemiology and experimental models: pancreatic steatosis and pancreatic cancer. EBioMedicine. 2022;79.

  8. Bi Y, Wang JL, Li ML, Zhou J, Sun XL. The association between pancreas steatosis and metabolic syndrome: a systematic review and meta-analysis. Diab/Metab Res Rev. 2019;35(5):e3142.

    Google Scholar 

  9. Tirkes T, Jeon CY, Li L, Joon AY, Seltman TA, Sankar M, et al. Association of pancreatic steatosis with chronic pancreatitis, obesity, and type 2 diabetes mellitus. Pancreas. 2019;48(3):420–6.

    PubMed  PubMed Central  CAS  Google Scholar 

  10. Rosso E, Casnedi S, Pessaux P, Oussoultzoglou E, Panaro F, Mahfud M, et al. The role of fatty pancreas and of BMI in the occurrence of pancreatic fistula after pancreaticoduodenectomy. J Gastrointest Surg. 2009;13:1845–51.

    PubMed  Google Scholar 

  11. Paul J, Shihaz AVH. Pancreatic steatosis: a new diagnosis and therapeutic challenge in gastroenterology. Arq Gastroenterol. 2020;57:216–20.

    PubMed  Google Scholar 

  12. Mahyoub MA, Elhoumed M, Maqul AH, Almezgagi M, Abbas M, Jiao Y, et al. Fatty infiltration of the pancreas: a systematic concept analysis. Front Med (Lausanne). 2023;10:1227188.

    PubMed  Google Scholar 

  13. Evert AB, Boucher JL, Cypress M, Dunbar SA, Franz MJ, Mayer-Davis EJ, et al. Nutrition therapy recommendations for the management of adults with diabetes. Diabetes Care. 2014;37(Suppl 1):S120–43.

    PubMed  Google Scholar 

  14. Thomas DE, Elliott EJ. The use of low-glycaemic index diets in diabetes control. Br J Nutr. 2010;104(6):797–802.

    PubMed  CAS  Google Scholar 

  15. Feliciano Pereira P, das, Graças de Almeida C, Alfenas Rde C. Glycemic index role on visceral obesity, subclinical inflammation and associated chronic diseases. Nutricion hospitalaria. 2014;30(2):237– 43.

  16. Parker A, Kim Y. The effect of low glycemic index and glycemic load diets on hepatic fat mass, insulin resistance, and blood lipid panels in individuals with nonalcoholic fatty liver disease. Metab Syndr Relat Disord. 2019;17(8):389–96.

    PubMed  CAS  Google Scholar 

  17. Van Der Merwe SW, Van Wanrooij RL, Bronswijk M, Everett S, Lakhtakia S, Rimbas M, et al. Therapeutic endoscopic ultrasound: European society of Gastrointestinal endoscopy (ESGE) guideline. Endoscopy. 2022;54(02):185–205.

    PubMed  Google Scholar 

  18. Willett W. Nutritional epidemiology. Oxford University Press; 2012.

  19. Mirmiran P, Esfahani FH, Mehrabi Y, Hedayati M, Azizi F. Reliability and relative validity of an FFQ for nutrients in the Tehran lipid and glucose study. Public Health Nutr. 2010;13(5):654–62.

    PubMed  Google Scholar 

  20. Atkinson FS, Foster-Powell K, Brand-Miller JC. International tables of glycemic index and glycemic load values: 2008. Diabetes Care. 2008;31(12):2281–3.

    PubMed  PubMed Central  Google Scholar 

  21. Neuhouser ML, Tinker LF, Thomson C, Caan B, Van Horn L, Snetselaar L, et al. Development of a glycemic index database for food frequency questionnaires used in epidemiologic studies. J Nutr. 2006;136(6):1604–9.

    PubMed  CAS  Google Scholar 

  22. Foster-Powell K, Holt SH, Brand-Miller JC. International table of glycemic index and glycemic load values: 2002. Am J Clin Nutr. 2002;76(1):5–56.

    PubMed  CAS  Google Scholar 

  23. The University of. Sydney glycemic index and GI database. https://glycemicindexcom/. 2005.

  24. Taleban F, Esmaeili M. Glycemic index of Iranian foods. National Nutrition and Food Technology Research Institute publication; 1999.

  25. Tucker LA. Fiber intake and insulin resistance in 6374 adults: the role of abdominal obesity. Nutrients. 2018;10(2).

  26. Weickert MO, Pfeiffer AFH. Impact of dietary fiber consumption on insulin resistance and the prevention of type 2 diabetes. J Nutr. 2018;148(1):7–12.

    PubMed  Google Scholar 

  27. Nie C, Yang T, Wang Z, Suolang D, Wang S, Baima K, et al. Dietary patterns and gallstone risks in Chinese adults: A Cross-sectional analysis of the China Multi-Ethnic cohort study. J Epidemiol. 2023;33(9):471–7.

    PubMed  PubMed Central  Google Scholar 

  28. Shivakoti R, Biggs ML, Djoussé L, Durda PJ, Kizer JR, Psaty B, et al. Intake and sources of dietary fiber, inflammation, and cardiovascular disease in older US adults. JAMA Netw Open. 2022;5(3):e225012.

    PubMed  PubMed Central  Google Scholar 

  29. van Geenen EJ, Smits MM, Schreuder TC, van der Peet DL, Bloemena E, Mulder CJ. Nonalcoholic fatty liver disease is related to nonalcoholic fatty pancreas disease. Pancreas. 2010;39(8):1185–90.

    PubMed  Google Scholar 

  30. Ahmed B, Sultana R, Greene MW. Adipose tissue and insulin resistance in obese. Biomed Pharmacother. 2021;137:111315.

    PubMed  CAS  Google Scholar 

  31. Kahn BB, Flier JS. Obesity and insulin resistance. J Clin Investig. 2000;106(4):473–81.

    PubMed  PubMed Central  CAS  Google Scholar 

  32. Weng S, Zhou J, Chen X, Sun Y, Mao Z, Chai K. Prevalence and factors associated with nonalcoholic fatty pancreas disease and its severity in China. Medicine. 2018;97(26):e11293.

    PubMed  PubMed Central  Google Scholar 

  33. van der Zijl NJ, Goossens GH, Moors CC, van Raalte DH, Muskiet MH, Pouwels PJ, et al. Ectopic fat storage in the pancreas, liver, and abdominal fat depots: impact on β-cell function in individuals with impaired glucose metabolism. J Clin Endocrinol Metab. 2011;96(2):459–67.

    PubMed  Google Scholar 

  34. Lee JS, Kim SH, Jun DW, Han JH, Jang EC, Park JY, et al. Clinical implications of fatty pancreas: correlations between fatty pancreas and metabolic syndrome. World J Gastroenterol. 2009;15(15):1869–75.

    PubMed  PubMed Central  CAS  Google Scholar 

  35. Vázquez-Jiménez JG, Roura-Guiberna A, Jiménez-Mena LR, Olivares-Reyes JA. Role of free fatty acids on insulin resistance. Gac Med Mex. 2017;153(7):852–63.

    PubMed  Google Scholar 

  36. Willett W, Manson J, Liu S. Glycemic index, glycemic load, and risk of type 2 diabetes1,2,3. Am J Clin Nutr. 2002;76(1):S274–80.

    Google Scholar 

  37. Brand-Miller JC, Holt SH, Pawlak DB, McMillan J. Glycemic index and obesity. Am J Clin Nutr. 2002;76(1):s281–5.

    Google Scholar 

  38. Mathur A, Marine M, Lu D, Swartz-Basile DA, Saxena R, Zyromski NJ, et al. Nonalcoholic fatty pancreas disease. HPB: Official J Int Hepato Pancreato Biliary Association. 2007;9(4):312–8.

    Google Scholar 

  39. Liu L, Mei M, Yang S, Li Q. Roles of chronic low-grade inflammation in the development of ectopic fat deposition. Mediat Inflamm. 2014;2014:418185.

    Google Scholar 

  40. Ceriello A. Postprandial hyperglycemia and diabetes complications: is it time to treat?? Diabetes. 2005;54(1):1–7.

    PubMed  CAS  Google Scholar 

  41. Dickinson S, Hancock DP, Petocz P, Ceriello A, Brand-Miller J. High–glycemic index carbohydrate increases nuclear factor-κB activation in mononuclear cells of young, lean healthy subjects. Am J Clin Nutr. 2008;87(5):1188–93.

    PubMed  CAS  Google Scholar 

  42. DiNicolantonio JJ, Mehta V, Onkaramurthy N, O’Keefe JH. Fructose-induced inflammation and increased cortisol: A new mechanism for how sugar induces visceral adiposity. Prog Cardiovasc Dis. 2018;61(1):3–9.

    PubMed  Google Scholar 

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Acknowledgements

This study is related to the project NO. 1402/73348 from Student Research Committee, Shahid Beheshti University of Medical Sciences, Tehran, Iran. We also appreciate the “Student Research Committee” and “Research & Technology Chancellor” in Shahid Beheshti University of Medical Sciences for their financial support of this study.

Funding

This project was funded by grant NO. 1402/73348 from Student Research Committee, Shahid Beheshti University of Medical Sciences, Tehran, Iran. The funding body has no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.

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Authors and Affiliations

  1. Student Research Committee, Department of Clinical Nutrition and dietetics, Faculty of Nutrition Sciences and Food Technology, National Nutrition and Food Technology Research Institute, Shahid Beheshti University of Medical Sciences, Tehran, Iran

    Mohammad Bahrizadeh

  2. Department of Clinical Nutrition and dietetics, Faculty of Nutrition Sciences and Food Technology, National Nutrition and Food Technology Research Institute, Shahid Beheshti University of Medical Sciences, West Arghavan St. Farahzadi Blvd., Sharake Qods, Tehran, Iran

    Mohammad Bahrizadeh, Danial Fotros, Maedeh Chegini & Azita Hekmatdoost

  3. Research Institute for Gastroenterology and Liver Diseases of Taleghani Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran

    Amir Sadeghi

  4. Department of Nutrition Research, National Nutrition and Food Technology Research Institute, Faculty of Nutrition Sciences and Food Technology, Shahid Beheshti University of Medical Sciences, West Arghavan St. Farahzadi Blvd., Sharake Qods, Tehran, Iran

    Zahra Yari

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Contributions

Conceptualization, ZY and AH; Formal analysis, ZY; Methodology, MB, MC and AS; Project administration, DF and AH; Writing– original draft, MB, DF and ZY; Writing– review & editing, ZY and AH. All authors read and approved.

Corresponding authors

Correspondence to Azita Hekmatdoost or Zahra Yari.

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The authors declare no competing interests.

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The study was approved by the Research Ethics Committee of Shahid Beheshti Medical University of Iran, under protocol number IR.SBMU.NNFTRI.REC.1402.689. All methods were carried out in accordance with relevant guidelines and regulations and all participants enrolled in the study provided written informed consent.

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Bahrizadeh, M., Fotros, D., Chegini, M. et al. Association of dietary glycemic index and glycemic load with pancreatic steatosis: a case control study. BMC Endocr Disord 25, 89 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12902-025-01909-0

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BMC Endocrine Disorders

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