1
|
Colantoni A, Bucci T, Cocomello N, Angelico F, Ettorre E, Pastori D, Lip GYH, Del Ben M, Baratta F. Lipid-based insulin-resistance markers predict cardiovascular events in metabolic dysfunction associated steatotic liver disease. Cardiovasc Diabetol 2024; 23:175. [PMID: 38769519 PMCID: PMC11106932 DOI: 10.1186/s12933-024-02263-6] [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: 03/05/2024] [Accepted: 05/03/2024] [Indexed: 05/22/2024] Open
Abstract
BACKGROUND Insulin resistance (IR) is the cornerstone of Metabolic Dysfunction Associated Steatotic Liver Disease (MASLD), pathophysiologically being the key link between MASLD, metabolic disorders, and cardiovascular (CV) diseases. There are no prospective studies comparing the predictive values of different markers of insulin resistance (IR) in identifying the presence of MASLD and the associated risk of cardiovascular events (CVEs). METHODS Post hoc analysis of the prospective Plinio Study, involving dysmetabolic patients evaluated for the presence of MASLD. The IR markers considered were Homeostatic Model Assessment for IR (HOMA-IR), Triglycerides-Glycemia (TyG) index, Triglycerides to High-Density Lipoprotein Cholesterol ratio (TG/HDL-C), Lipid Accumulation Product (LAP) and Visceral Adiposity Index (VAI). Receiver operative characteristic (ROC) analyses were performed to find the optimal cut-offs of each IR marker for detecting MASLD and predicting CVEs in MASLD patients. Logistic and Cox multivariable regression analyses were performed, after dichotomizing the IR markers based on the optimal cut-offs, to assess the factors independently associated with MASLD and the risk of CVEs. RESULTS The study included 772 patients (age 55.6 ± 12.1 years, 39.4% women), of whom 82.8% had MASLD. VAI (Area Under the Curve [AUC] 0.731), TyG Index (AUC 0.723), and TG/HDL-C ratio (AUC: 0.721) predicted MASLD but was greater with HOMA-IR (AUC: 0.792) and LAP (AUC: 0.787). After a median follow-up of 48.7 (25.4-75.8) months, 53 MASLD patients experienced CVEs (1.8%/year). TyG index (AUC: 0.630), LAP (AUC: 0.626), TG/HDL-C (AUC: 0.614), and VAI (AUC: 0.590) demonstrated comparable, modest predictive values in assessing the CVEs risk in MASLD patients. CONCLUSION In dysmetabolic patients HOMA-IR and LAP showed the best accuracy in detecting MASLD. The possible use of lipid-based IR markers in stratifying the CV risk in patients with MASLD needs further validation in larger cohorts.
Collapse
Affiliation(s)
- Alessandra Colantoni
- Department of Clinical Internal, Anesthesiologic and Cardiovascular Sciences, Sapienza University of Rome, Rome, Italy
- Department of Human Anatomy, Histology, Forensic Medicine and Orthopedics, Sapienza University of Rome, Rome, Italy
| | - Tommaso Bucci
- Liverpool Centre for Cardiovascular Science at University of Liverpool, Liverpool John Moores University and Liverpool and Heart and Chest Hospital, Liverpool, UK
- Department of General and Specialized Surgery, Sapienza University of Rome, Rome, Italy
| | - Nicholas Cocomello
- Department of Clinical Internal, Anesthesiologic and Cardiovascular Sciences, Sapienza University of Rome, Rome, Italy
- Department of Human Anatomy, Histology, Forensic Medicine and Orthopedics, Sapienza University of Rome, Rome, Italy
| | - Francesco Angelico
- Department of Clinical Internal, Anesthesiologic and Cardiovascular Sciences, Sapienza University of Rome, Rome, Italy
| | - Evaristo Ettorre
- Department of Clinical Internal, Anesthesiologic and Cardiovascular Sciences, Sapienza University of Rome, Rome, Italy
| | - Daniele Pastori
- Department of Clinical Internal, Anesthesiologic and Cardiovascular Sciences, Sapienza University of Rome, Rome, Italy
| | - Gregory Y H Lip
- Liverpool Centre for Cardiovascular Science at University of Liverpool, Liverpool John Moores University and Liverpool and Heart and Chest Hospital, Liverpool, UK
- Department of Clinical Medicine, Danish Center for Health Services Research, Aalborg University, Aalborg, Denmark
| | - Maria Del Ben
- Department of Clinical Internal, Anesthesiologic and Cardiovascular Sciences, Sapienza University of Rome, Rome, Italy
| | - Francesco Baratta
- Department of Clinical Internal, Anesthesiologic and Cardiovascular Sciences, Sapienza University of Rome, Rome, Italy.
| |
Collapse
|
2
|
Bahijri S, Eldakhakhny B, Enani S, Ajabnoor G, Al-Mowallad AS, Alsheikh L, Alhozali A, Alamoudi AA, Borai A, Tuomilehto J. Fibroblast Growth Factor 21: A More Effective Biomarker Than Free Fatty Acids and Other Insulin Sensitivity Measures for Predicting Non-alcoholic Fatty Liver Disease in Saudi Arabian Type 2 Diabetes Patients. Cureus 2023; 15:e50524. [PMID: 38222178 PMCID: PMC10787595 DOI: 10.7759/cureus.50524] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/14/2023] [Indexed: 01/16/2024] Open
Abstract
Background Non-alcoholic fatty liver disease (NAFLD) is more prevalent among individuals with type 2 diabetes (T2DM), elevating their risk of cardiovascular diseases (CVDs) and premature mortality. There is a need to modify treatment strategies to prevent or delay these adverse outcomes. Currently, there are no sensitive or specific biomarkers for predicting NAFLD in Saudi T2DM patients. Therefore, we aimed to explore the possibility of using fibroblast growth factor 21 (FGF-21), free fatty acids (FFAs), homeostatic model assessment for insulin resistance (HOMA-IR), and quantitative insulin sensitivity check index (QUICKI) as possible markers. Methodology In this study, a total of 67 T2DM patients were recruited. NAFLD was detected by ultrasonography in 28 patients. Plasma glucose, FFAs, FGF-21, and serum insulin were measured in fasting blood samples. HOMA-IR and QUICKI were calculated. The means of the two groups with and without NAFLD were statistically compared. The receiver operating characteristics (ROC) curve and the area under the curve (AUC) were used to assess the ability to identify NAFLD. Results The mean levels of FGF-21 and HOMA-IR were significantly higher and that of QUICKI was significantly lower in patients with NAFLD than in those without (p < 0.001, p = 0.023, and p = 0.018, respectively). FGF-21 had the highest AUC to identify NAFLD (AUC = 0.981, 95% confidence interval = 0.954-1, P < 0.001). The AUCs for HOMA-IR, QUICKI, and FFA were <0.7. The highest sensitivity, specificity, positive likelihood ratio, and the lowest negative likelihood ratio were found when FGF-21 was used to predict NAFLD. Conclusions FGF-21 may be used as a biomarker to predict NAFLD in people with T2DM due to its high sensitivity and specificity compared to the other markers.
Collapse
Affiliation(s)
- Suhad Bahijri
- Department of Clinical Biochemistry, King Abdulaziz University Faculty of Medicine, Jeddah, SAU
- Saudi Diabetes Research Group, Deanship of Scientific Research, King Abdulaziz University, Jeddah, SAU
- Food, Nutrition and Lifestyle Unit, King Fahd Medical Research Centre, King Abdulaziz University, Jeddah, SAU
| | - Basmah Eldakhakhny
- Department of Clinical Biochemistry, King Abdulaziz University Faculty of Medicine, Jeddah, SAU
- Saudi Diabetes Research Group, Deanship of Scientific Research, King Abdulaziz University, Jeddah, SAU
- Food, Nutrition and Lifestyle Unit, King Fahd Medical Research Centre, King Abdulaziz University, Jeddah, SAU
| | - Sumia Enani
- Department of Food and Nutrition, Faculty of Human Sciences and Design, King Abdulaziz University, Jeddah, SAU
- Saudi Diabetes Research Group, Deanship of Scientific Research, King Abdulaziz University, Jeddah, SAU
- Food, Nutrition and Lifestyle Unit, King Fahd Medical Research Centre, King Abdulaziz University, Jeddah, SAU
| | - Ghada Ajabnoor
- Department of Clinical Biochemistry, King Abdulaziz University Faculty of Medicine, Jeddah, SAU
- Saudi Diabetes Research Group, Deanship of Scientific Research, King Abdulaziz University, Jeddah, SAU
- Food, Nutrition and Lifestyle Unit, King Fahd Medical Research Centre, King Abdulaziz University, Jeddah, SAU
| | - Alaa S Al-Mowallad
- Department of Clinical Biochemistry, King Abdulaziz University Faculty of Medicine, Jeddah, SAU
| | - Lubna Alsheikh
- Department of Biochemistry, King Abdulaziz University, Jeddah, SAU
| | - Amani Alhozali
- Department of Internal Medicine, King Abdulaziz University Hospital, Jeddah, SAU
| | - Aliaa A Alamoudi
- Department of Clinical Biochemistry, King Abdulaziz University Faculty of Medicine, Jeddah, SAU
- Saudi Diabetes Research Group, Deanship of Scientific Research, King Abdulaziz University, Jeddah, SAU
| | - Anwar Borai
- King Abdullah International Medical Research Center (KAIMRC), King Saud Bin Abdulaziz University for Health Sciences, Jeddah, SAU
- Saudi Diabetes Research Group, Deanship of Scientific Research, King Abdulaziz University, Jeddah, SAU
| | - Jaakko Tuomilehto
- Department of Public Health, University of Helsinki, Helsinki, FIN
- Public Health Promotion Unit, Finnish Institute for Health and Welfare, Helsinki, FIN
| |
Collapse
|