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Khamseh ME, Malek M, Jahangiri S, Nobarani S, Hekmatdoost A, Salavatizadeh M, Soltanieh S, Chehrehgosha H, Taheri H, Montazeri Z, Attaran F, Ismail-Beigi F, Alaei-Shahmiri F. Insulin Resistance/Sensitivity Measures as Screening Indicators of Metabolic-Associated Fatty Liver Disease and Liver Fibrosis. Dig Dis Sci 2024; 69:1430-1443. [PMID: 38438774 DOI: 10.1007/s10620-024-08309-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Accepted: 01/19/2024] [Indexed: 03/06/2024]
Abstract
BACKGROUND Measures of insulin resistance (IR)/sensitivity (IS) are emerging tools to identify metabolic-associated fatty liver disease (MAFLD). However, the comprehensive assessment of the performance of various indicators is limited. Moreover, the utility of measures of IR/IS in detecting liver fibrosis remains unclear. AIMS To evaluate the predictive ability of seventeen IR/IS and two beta cell function indices to identify MAFLD and liver fibrosis. METHODS A cross-sectional study was conducted on individuals aged 25-75 years. Transient elastography was used to estimate liver stiffness and controlled attenuation parameter. The following measures were computed: homeostatic model assessment (HOMA/HOMA2) for IR, IS, and beta cell function; QUICKI; Bennett index; glucose/insulin; FIRI; McAuley index; Reynaud index; SPISE index; TyG; TyG-BMI; TyG-WC; TyG-WHtR; TG/HDL; and METS-IR. Subgroup analyses were performed according to age, gender, diabetes status, and body weight. RESULTS A total of 644 individuals were included in our analysis. MAFLD and significant liver fibrosis were detected in 320 (49.7%) and 80 (12.4%) of the participants, respectively. All measures of IR/IS identified MAFLD and liver fibrosis. However, TyG-WC, TyG-BMI, and TyG-WHtR were the top three indicators that identified MAFLD. Measures that include insulin level in their mathematical calculation, namely, Raynaud index, HOMA-IR, HOMA 2-IR, FIRI, and QUICKI had the best performance in identifying liver fibrosis in the entire population, as well as among the study subgroups. CONCLUSIONS TyG-WC, TyG-BMI, and TyG-WHtR were the best predictors of MAFLD. Insulin-based measures had better performances in the detection of advanced fibrosis. This was independent of age, gender, obesity, or diabetes status.
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Affiliation(s)
- Mohammad E Khamseh
- Endocrine Research Center, Institute of Endocrinology and Metabolism, Iran University of Medical Sciences (IUMS), Tehran, Iran
| | - Mojtaba Malek
- Research Center for Prevention of Cardiovascular Disease, Institute of Endocrinology and Metabolism, Iran University of Medical Sciences (IUMS), Tehran, Iran
| | - Soodeh Jahangiri
- Endocrine Research Center, Institute of Endocrinology and Metabolism, Iran University of Medical Sciences (IUMS), Tehran, Iran
| | - Sohrab Nobarani
- Endocrine Research Center, Institute of Endocrinology and Metabolism, Iran University of Medical Sciences (IUMS), Tehran, Iran
| | - Azita Hekmatdoost
- Department of Clinical Nutrition and Dietetics, Faculty of Nutrition and Food Technology, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Marieh Salavatizadeh
- Department of Clinical Nutrition and Dietetics, Faculty of Nutrition and Food Technology, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Samira Soltanieh
- Department of Clinical Nutrition and Dietetics, Faculty of Nutrition and Food Technology, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Haleh Chehrehgosha
- Endocrine Research Center, Institute of Endocrinology and Metabolism, Iran University of Medical Sciences (IUMS), Tehran, Iran
| | - Hoda Taheri
- Endocrine Research Center, Institute of Endocrinology and Metabolism, Iran University of Medical Sciences (IUMS), Tehran, Iran
| | - Zeinab Montazeri
- Endocrine Research Center, Institute of Endocrinology and Metabolism, Iran University of Medical Sciences (IUMS), Tehran, Iran
| | - Fereshteh Attaran
- Endocrine Research Center, Institute of Endocrinology and Metabolism, Iran University of Medical Sciences (IUMS), Tehran, Iran
| | - Faramarz Ismail-Beigi
- Department of Medicine, Case Western Reserve University, University Hospitals Cleveland Medical Center, Cleveland, OH, 44106, USA
| | - Fariba Alaei-Shahmiri
- Endocrine Research Center, Institute of Endocrinology and Metabolism, Iran University of Medical Sciences (IUMS), Tehran, Iran.
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Pretransplant HOMA-β Is Predictive of Insulin Independence in 7 Patients With Chronic Pancreatitis Undergoing Islet Autotransplantation. Transplant Direct 2022; 8:e1367. [PMID: 36204182 PMCID: PMC9529061 DOI: 10.1097/txd.0000000000001367] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Revised: 06/24/2022] [Accepted: 07/13/2022] [Indexed: 11/26/2022] Open
Abstract
Islet and β-cell function is intrinsic to glucose homeostasis. Pancreatectomy and islet autotransplantation (PIAT) for chronic pancreatitis (CP) treatment is a useful model for assessing islet function in the absence of immune-suppression and to perform extensive presurgical metabolic evaluations not possible from deceased donors. We recently showed that in CP-PIAT patients, preoperative islet identity loss presented with postoperative glycemic loss. Here, we examine presurgical islet function using Homeostatic Model Assessment-Beta Cell Function (%) (HOMA-β) and glycemic variables and compared them with postsurgical insulin independence and their predicted alignment with Secretory Unit of Islet Transplant Objects (SUITO) and beta cell score after transplantation (BETA-2) scores.
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Chen X, Xiao J, Pang J, Chen S, Wang Q, Ling W. Pancreatic β-Cell Dysfunction Is Associated with Nonalcoholic Fatty Liver Disease. Nutrients 2021; 13:nu13093139. [PMID: 34579016 PMCID: PMC8468093 DOI: 10.3390/nu13093139] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Revised: 09/03/2021] [Accepted: 09/08/2021] [Indexed: 12/22/2022] Open
Abstract
Background: Nonalcoholic fatty liver disease (NAFLD) is associated with decreased insulin sensitivity. However, the association between NAFLD and pancreatic β-cell function is still ambiguous. Here, we assessed whether pancreatic β-cell function is associated with NAFLD. Method: The data of NHANES III from 1988 to 1994 were used. NAFLD was diagnosed when subjects had ultrasonographically hepatic steatosis without other liver diseases. Disposition index (DI) was employed to assess pancreatic β-cell function. A total of 6168 participants were included in this study. Results: NAFLD participants had much higher HOMA2-%B (weighted mean, 124.1; standard error, 1.8) than the non-NAFLD participants (weighted mean, 100.7; standard error, 0.9). However, when evaluating the β-cell function in the context of insulin resistance by using DI index, DI levels were much lower in NAFLD subjects (weighted mean, 79.5; standard error, 1.0) compared to non-NAFLD (weighted mean, 95.0; standard error, 0.8). Multivariate logistic regression analyses showed that DI was inversely associated with NAFLD prevalence. The adjusted OR (95% CI) for quartile 1 versus quartile 4 was 1.81 (1.31–2.50) (p < 0.001 for trend). Moreover, DI was also inversely associated with the presence of moderate to severe hepatic steatosis. The multivariable-adjusted ORs across quartiles of DI were 2.47, 1.44, 0.96 and 1.00 for the presence of moderate to severe hepatic steatosis (p < 0.001 for trend). Conclusions: Pancreatic β-cell function might be a new predictor for the presence of NAFLD, and insufficient compensatory β-cell function is associated with NAFLD.
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Affiliation(s)
- Xu Chen
- Department of Nutrition, School of Public Health, Ningxia Medical University, Yinchuan 750004, China
- Department of Nutrition, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Jinghe Xiao
- Department of Nutrition, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Juan Pang
- Department of Nutrition, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Shen Chen
- Department of Toxicology, School of Public Health, Sun Yat-Sen University, Guangzhou 510080, China
| | - Qing Wang
- Department of Toxicology, School of Public Health, Sun Yat-Sen University, Guangzhou 510080, China
| | - Wenhua Ling
- Department of Nutrition, School of Public Health, Ningxia Medical University, Yinchuan 750004, China
- Department of Nutrition, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
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