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He J, Li X, Yan M, Chen X, Sun C, Tan J, Song Y, Xu H, Wu L, Yang Z. Inulin Reduces Kidney Damage in Type 2 Diabetic Mice by Decreasing Inflammation and Serum Metabolomics. J Diabetes Res 2024; 2024:1222395. [PMID: 38725443 PMCID: PMC11081752 DOI: 10.1155/2024/1222395] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 03/28/2024] [Accepted: 03/30/2024] [Indexed: 05/12/2024] Open
Abstract
This study is aimed at assessing the impact of soluble dietary fiber inulin on the treatment of diabetes-related chronic inflammation and kidney injury in mice with type 2 diabetes (T2DM). The T2DM model was created by feeding the Institute of Cancer Research (ICR) mice a high-fat diet and intraperitoneally injecting them with streptozotocin (50 mg/kg for 5 consecutive days). The thirty-six ICR mice were divided into three dietary groups: the normal control (NC) group, the T2DM (DM) group, and the DM + inulin diet (INU) group. The INU group mice were given inulin at the dose of 500 mg/kg gavage daily until the end of the 12th week. After 12 weeks, the administration of inulin resulted in decreased serum levels of fasting blood glucose (FBG), low-density lipoprotein cholesterol (LDL-C), blood urea nitrogen (BUN), and creatinine (CRE). The administration of inulin not only ameliorated renal injury but also resulted in a reduction in the mRNA expressions of inflammatory factors in the spleen and serum oxidative stress levels, when compared to the DM group. Additionally, inulin treatment in mice with a T2DM model led to a significant increase in the concentrations of three primary short-chain fatty acids (SCFAs) (acetic acid, propionic acid, and butyric acid), while the concentration of advanced glycation end products (AGEs), a prominent inflammatory factor in diabetes, exhibited a significant decrease. The results of untargeted metabolomics indicate that inulin has the potential to alleviate inflammatory response and kidney damage in diabetic mice. This beneficial effect is attributed to its impact on various metabolic pathways, including glycerophospholipid metabolism, taurine and hypotaurine metabolism, arginine biosynthesis, and tryptophan metabolism. Consequently, oral inulin emerges as a promising treatment option for diabetes and kidney injury.
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Affiliation(s)
- Jiayuan He
- Health Testing Center, Zhenjiang Center for Disease Control and Prevention, Zhenjiang 212002, China
| | - Xiang Li
- Medical Laboratory Department, Huai'an Second People's Hospital, Huai'an 223022, China
| | - Man Yan
- Department of Laboratory Medicine, School of Medicine, Jiangsu University, Zhenjiang 212013, China
| | - Xinsheng Chen
- Hospital Infection-Disease Control Department, Zhenjiang First People's Hospital, Zhenjiang 212002, China
| | - Chang Sun
- Department of Laboratory Medicine, School of Medicine, Jiangsu University, Zhenjiang 212013, China
| | - Jiajun Tan
- Department of Laboratory Medicine, School of Medicine, Jiangsu University, Zhenjiang 212013, China
| | - Yinsheng Song
- Health Testing Center, Zhenjiang Center for Disease Control and Prevention, Zhenjiang 212002, China
| | - Hong Xu
- Health Testing Center, Zhenjiang Center for Disease Control and Prevention, Zhenjiang 212002, China
| | - Liang Wu
- Department of Laboratory Medicine, School of Medicine, Jiangsu University, Zhenjiang 212013, China
| | - Zhengnan Yang
- Department of Clinical Laboratory, Yizheng Hospital, Nanjing Drum Tower Hospital Group, Yizheng 210008, China
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Garcia-Carretero R, Vazquez-Gomez O, Gil-Prieto R, Gil-de-Miguel A. Insulin resistance is a cardiovascular risk factor in hypertensive adults without type 2 diabetes mellitus. Wien Klin Wochenschr 2024; 136:101-109. [PMID: 37814058 DOI: 10.1007/s00508-023-02278-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Accepted: 08/27/2023] [Indexed: 10/11/2023]
Abstract
BACKGROUND Metabolic syndrome refers to the association among several cardiovascular risk factors: obesity, dyslipidemia, hyperglycemia, and hypertension. It is associated with increased cardiovascular risk and the development of type 2 diabetes mellitus. Insulin resistance is the underlying mechanism of metabolic syndrome, although its role in increased cardiovascular risk has not been directly identified. OBJECTIVE We investigated the association between insulin resistance and increased cardiovascular risk in hypertensive adults without diabetes mellitus. DESIGN AND PARTICIPANTS We enrolled participants without diabetes from an outpatient setting in a retrospective, longitudinal study. Several demographic, clinical, and laboratory parameters were recorded during the observation period. Plasma insulin and homeostatic model assessment for insulin resistance (HOMA-IR) were used to determine insulin resistance and four cardiovascular events (acute coronary disease, acute cerebrovascular disease, incident heart failure, and cardiovascular mortality) were combined into a single outcome. Logistic regression and Cox proportional hazards models were fitted to evaluate the association between covariates and outcomes. RESULTS We included 1899 hypertensive adults without diabetes with an average age of 53 years (51.3% women, 23% had prediabetes, and 64.2% had metabolic syndrome). In a logistic regression analysis, male sex (odds ratio, OR = 1.66) having high levels of low-density lipoprotein (LDL, OR = 1.01), kidney function (OR = 0.97), and HOMA-IR (OR = 1.06) were associated with the incidence of cardiovascular events; however, in a survival multivariate analysis, only HOMA-IR (hazard ratio, HR 1.4, 95% confidence interval, CI: 1.05-1.87, p = 0.02) and body mass index (HR 1.05, 95% CI: 1.02-1.08, p = 0.002) were considered independent prognostic variables for the development of incident cardiovascular events. CONCLUSION Insulin resistance and obesity are useful for assessing cardiovascular risk in hypertensive people without diabetes but with preserved kidney function. This work demonstrates the predictive value of the measurement of insulin, and therefore of insulin resistance, in an outpatient setting and attending to high-risk patients.
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Affiliation(s)
- Rafael Garcia-Carretero
- Department of Internal Medicine, Mostoles University Hospital, Rey Juan Carlos University (Madrid), Calle Rio Jucar, s/n, 28935, Mostoles (Madrid), Spain.
| | - Oscar Vazquez-Gomez
- Department of Internal Medicine, Mostoles University Hospital, Rey Juan Carlos University (Madrid), Calle Rio Jucar, s/n, 28935, Mostoles (Madrid), Spain
| | - Ruth Gil-Prieto
- Department of Preventive Medicine and Public Health, Rey Juan Carlos University (Madrid), Madrid, Spain
| | - Angel Gil-de-Miguel
- Department of Preventive Medicine and Public Health, Rey Juan Carlos University (Madrid), Madrid, Spain
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Mohsen F, Al-Absi HRH, Yousri NA, El Hajj N, Shah Z. A scoping review of artificial intelligence-based methods for diabetes risk prediction. NPJ Digit Med 2023; 6:197. [PMID: 37880301 PMCID: PMC10600138 DOI: 10.1038/s41746-023-00933-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2023] [Accepted: 09/25/2023] [Indexed: 10/27/2023] Open
Abstract
The increasing prevalence of type 2 diabetes mellitus (T2DM) and its associated health complications highlight the need to develop predictive models for early diagnosis and intervention. While many artificial intelligence (AI) models for T2DM risk prediction have emerged, a comprehensive review of their advancements and challenges is currently lacking. This scoping review maps out the existing literature on AI-based models for T2DM prediction, adhering to the PRISMA extension for Scoping Reviews guidelines. A systematic search of longitudinal studies was conducted across four databases, including PubMed, Scopus, IEEE-Xplore, and Google Scholar. Forty studies that met our inclusion criteria were reviewed. Classical machine learning (ML) models dominated these studies, with electronic health records (EHR) being the predominant data modality, followed by multi-omics, while medical imaging was the least utilized. Most studies employed unimodal AI models, with only ten adopting multimodal approaches. Both unimodal and multimodal models showed promising results, with the latter being superior. Almost all studies performed internal validation, but only five conducted external validation. Most studies utilized the area under the curve (AUC) for discrimination measures. Notably, only five studies provided insights into the calibration of their models. Half of the studies used interpretability methods to identify key risk predictors revealed by their models. Although a minority highlighted novel risk predictors, the majority reported commonly known ones. Our review provides valuable insights into the current state and limitations of AI-based models for T2DM prediction and highlights the challenges associated with their development and clinical integration.
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Affiliation(s)
- Farida Mohsen
- College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, 34110, Doha, Qatar
| | - Hamada R H Al-Absi
- College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, 34110, Doha, Qatar
| | - Noha A Yousri
- Genetic Medicine, Weill Cornell Medicine-Qatar, Qatar Foundation, Doha, Qatar
- College of Health and Life Sciences, Hamad Bin Khalifa University, Qatar Foundation, 34110, Doha, Qatar
- Computer and Systems Engineering, Alexandria University, Alexandria, Egypt
| | - Nady El Hajj
- College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, 34110, Doha, Qatar
- College of Health and Life Sciences, Hamad Bin Khalifa University, Qatar Foundation, 34110, Doha, Qatar
| | - Zubair Shah
- College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, 34110, Doha, Qatar.
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Eşsiz UE, Yüregir OH, Saraç E. Applying data mining techniques to predict vitamin D deficiency in diabetic patients. Health Informatics J 2023; 29:14604582231214864. [PMID: 37963409 DOI: 10.1177/14604582231214864] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2023]
Abstract
Vitamin D is among the vitamins necessary for both adults' and children's health. It plays a significant role in calcium absorption, the immune system, cell proliferation and differentiation, bone protection, skeletal health, rickets, muscle health, heart health, disease pathogenesis and severity, glucose metabolism, glucose intolerance, varying insulin secretion, and diabetes. Because the 25-hydroxyvitamin D (25OHD) test, which is used to measure vitamin D is expensive and may not be covered in healthcare benefits in many countries, this study aims to predict vitamin D deficiency in diabetic patients. The prediction method is based on data mining techniques combined with feature selection by using historical electronic health records. The results were compared with a filter-based feature selection algorithm, namely relief-F. Non-valuable features were eliminated effectively with the relief-F feature selection method without any performance loss in classification. The performances of the methods were evaluated using classification accuracy (ACC), sensitivity, specificity, F1-score, precision, kappa results, and receiver operating characteristic (ROC) curves. The analyses have been conducted on a vitamin D dataset of diabetic patients and the results show that the highest classification accuracy of 97.044% was obtained for the support vector machines (SVM) model using radial kernel that contains 18 features.
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Affiliation(s)
- Uğur Engin Eşsiz
- Department of Industrial Engineering, Çukurova University, Adana, Turkey
| | - Oya Hacire Yüregir
- Department of Industrial Engineering, Çukurova University, Adana, Turkey
| | - Esra Saraç
- Department of Computer Engineering, Adana Alparslan Türkeş Science and Technology University, Adana, Turkey
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Ilari L, Piersanti A, Göbl C, Burattini L, Kautzky-Willer A, Tura A, Morettini M. Unraveling the Factors Determining Development of Type 2 Diabetes in Women With a History of Gestational Diabetes Mellitus Through Machine-Learning Techniques. Front Physiol 2022; 13:789219. [PMID: 35250610 PMCID: PMC8892139 DOI: 10.3389/fphys.2022.789219] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Accepted: 01/11/2022] [Indexed: 11/13/2022] Open
Abstract
Gestational diabetes mellitus (GDM) is a type of diabetes that usually resolves at the end of the pregnancy but exposes to a higher risk of developing type 2 diabetes mellitus (T2DM). This study aimed to unravel the factors, among those that quantify specific metabolic processes, which determine progression to T2DM by using machine-learning techniques. Classification of women who did progress to T2DM (labeled as PROG, n = 19) vs. those who did not (labeled as NON-PROG, n = 59) progress to T2DM has been performed by using Orange software through a data analysis procedure on a generated data set including anthropometric data and a total of 34 features, extracted through mathematical modeling/methods procedures. Feature selection has been performed through decision tree algorithm and then Naïve Bayes and penalized (L2) logistic regression were used to evaluate the ability of the selected features to solve the classification problem. Performance has been evaluated in terms of area under the operating receiver characteristics (AUC), classification accuracy (CA), precision, sensitivity, specificity, and F1. Feature selection provided six features, and based on them, classification was performed as follows: AUC of 0.795, 0.831, and 0.884; CA of 0.827, 0.813, and 0.840; precision of 0.830, 0.854, and 0.834; sensitivity of 0.827, 0.813, and 0.840; specificity of 0.700, 0.821, and 0.662; and F1 of 0.828, 0.824, and 0.836 for tree algorithm, Naïve Bayes, and penalized logistic regression, respectively. Fasting glucose, age, and body mass index together with features describing insulin action and secretion may predict the development of T2DM in women with a history of GDM.
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Affiliation(s)
- Ludovica Ilari
- Department of Information Engineering, Università Politecnica delle Marche, Ancona, Italy
| | - Agnese Piersanti
- Department of Information Engineering, Università Politecnica delle Marche, Ancona, Italy
| | - Christian Göbl
- Department of Obstetrics and Gynecology, Medical University of Vienna, Vienna, Austria
| | - Laura Burattini
- Department of Information Engineering, Università Politecnica delle Marche, Ancona, Italy
| | - Alexandra Kautzky-Willer
- Division of Endocrinology and Metabolism, Department of Internal Medicine III, Medical University of Vienna, Vienna, Austria
| | - Andrea Tura
- Metabolic Unit, CNR Institute of Neuroscience, Padua, Italy
| | - Micaela Morettini
- Department of Information Engineering, Università Politecnica delle Marche, Ancona, Italy
- *Correspondence: Micaela Morettini,
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García-Carretero R, Holgado-Cuadrado R, Barquero-Pérez Ó. Assessment of Classification Models and Relevant Features on Nonalcoholic Steatohepatitis Using Random Forest. ENTROPY (BASEL, SWITZERLAND) 2021; 23:763. [PMID: 34204225 PMCID: PMC8234908 DOI: 10.3390/e23060763] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/01/2021] [Revised: 06/12/2021] [Accepted: 06/14/2021] [Indexed: 12/24/2022]
Abstract
Nonalcoholic fatty liver disease (NAFLD) is the hepatic manifestation of metabolic syndrome and is the most common cause of chronic liver disease in developed countries. Certain conditions, including mild inflammation biomarkers, dyslipidemia, and insulin resistance, can trigger a progression to nonalcoholic steatohepatitis (NASH), a condition characterized by inflammation and liver cell damage. We demonstrate the usefulness of machine learning with a case study to analyze the most important features in random forest (RF) models for predicting patients at risk of developing NASH. We collected data from patients who attended the Cardiovascular Risk Unit of Mostoles University Hospital (Madrid, Spain) from 2005 to 2021. We reviewed electronic health records to assess the presence of NASH, which was used as the outcome. We chose RF as the algorithm to develop six models using different pre-processing strategies. The performance metrics was evaluated to choose an optimized model. Finally, several interpretability techniques, such as feature importance, contribution of each feature to predictions, and partial dependence plots, were used to understand and explain the model to help obtain a better understanding of machine learning-based predictions. In total, 1525 patients met the inclusion criteria. The mean age was 57.3 years, and 507 patients had NASH (prevalence of 33.2%). Filter methods (the chi-square and Mann-Whitney-Wilcoxon tests) did not produce additional insight in terms of interactions, contributions, or relationships among variables and their outcomes. The random forest model correctly classified patients with NASH to an accuracy of 0.87 in the best model and to 0.79 in the worst one. Four features were the most relevant: insulin resistance, ferritin, serum levels of insulin, and triglycerides. The contribution of each feature was assessed via partial dependence plots. Random forest-based modeling demonstrated that machine learning can be used to improve interpretability, produce understanding of the modeled behavior, and demonstrate how far certain features can contribute to predictions.
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Affiliation(s)
- Rafael García-Carretero
- Department of Signal Theory and Communications and Telematics Systems and Computing, Rey Juan Carlos University, 28935 Mostoles, Spain; (R.G.-C.); (R.H.-C.)
- Deparment of Internal Medicine, Mostoles University Hospital, 28935 Mostoles, Spain
| | - Roberto Holgado-Cuadrado
- Department of Signal Theory and Communications and Telematics Systems and Computing, Rey Juan Carlos University, 28935 Mostoles, Spain; (R.G.-C.); (R.H.-C.)
| | - Óscar Barquero-Pérez
- Department of Signal Theory and Communications and Telematics Systems and Computing, Rey Juan Carlos University, 28935 Mostoles, Spain; (R.G.-C.); (R.H.-C.)
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