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identify patients at high risk for developing severe COVID-19 manifestations and mortality like advanced age, male sex, obesity, hyperglycemia, and presence of comorbidities (diabetes mellitus (DM), cardiovascular disease (CVD) and chronic kidney disease
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Introduction Type 1 diabetes mellitus (DM) is an autoimmune disease in which T-lymphocytes attack insulin-producing beta cells in the pancreas ( 1 ). During the later stages of this progressive disease, pancreatic beta cells are massively
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Isala, Department of Internal Medicine, Zwolle, The Netherlands
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Langerhans Medical Research group, Zwolle, The Netherlands
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Department of General Practice, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
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Langerhans Medical Research group, Zwolle, The Netherlands
Department of Internal Medicine, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
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Isala, Department of Internal Medicine, Zwolle, The Netherlands
Department of Internal Medicine, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
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Introduction Among type 1 diabetes mellitus (T1DM) patients, subcutaneous (SC) insulin administration is associated with low portal insulin concentrations and a consequent hepatic underinsulinization ( 1 ). Hepatic underinsulinization has been
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Introduction Type 2 diabetes mellitus (T2DM) is a metabolic disease that induces substantial morbidity in affected patients ( 1 ). Chronic hyperglycemia predisposes patients to microvascular complications including retinopathy, neuropathy, and
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Introduction The prevalence of type 2 diabetes mellitus (T2DM) is increasing globally, and diabetic complications affect millions of people worldwide ( 1 , 2 ). Diabetic eye diseases, such as diabetic retinopathy (DR) and diabetic macular
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Introduction Type 2 diabetes mellitus (T2DM) is a chronic metabolic disease, characterized by the hyperglycemia level and insulin resistance in the body. T2DM is also considered to be induced by personal lifestyle, such as high consumption of
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Background: Fatty acid-binding protein 4 (FABP4) is an adipokine that plays significant roles in the development of insulin resistance and atherosclerosis. High levels of soluble tumor necrosis factor receptors (TNFRs) including TNFR1 and TNFR2 are associated with renal dysfunction and increased mortality in patients with diabetes mellitus (DM). However, the association between circulating levels of FABP4 and TNFRs remains unclear.
Methods: We investigated the associations of FABP4 with TNFRs and metabolic markers in Japanese patients with type 1 DM (T1DM, n=76, men/women: 31/45) and type 2 DM (T2DM, n=575, men/women: 312/263).
Results: FABP4 concentration was positively correlated with levels of TNFR1 and TNFR2 in both patients with T1DM and those with T2DM. Multivariable regression analyses showed that there were independent associations of FABP4 concentration with body mass index (BMI) and estimated glomerular filtration rate (eGFR) after adjustment of age and sex in both patients with T1DM and those with T2DM. FABP4 concentration was independently associated with circulating levels of TNFR1 and TNFR2 after adjustment of the confounders in patients with T2DM but not in those with T1DM. Similarly, levels of TNFR1 and TNFR2 were independently associated with FABP4 concentration after adjustment of age, sex, systolic blood pressure, duration of DM and levels of eGFR, high-density lipoprotein cholesterol and C-reactive protein in patients with T2DM but not in those with T1DM.
Conclusion: FABP4 concentration is independently associated with levels of TNFRs in patients with DM, but the association is more evident in patients with T2DM than in those with T1DM.
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with anti-PD-1 antibodies and hypophysitis (IH) commonly related to anti-CTLA-4 therapy, are the most frequent endocrine toxicities. ICI-induced insulin-deficient diabetes mellitus (DM) and primary adrenal insufficiency (PAI), though rare, can be life
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Introduction: Gestational diabetes mellitus (GDM) significantly affects pregnancy outcomes. Therefore, it is crucial to develop prediction models since they can guide timely interventions to reduce the incidence of GDM and its associated adverse effects.
Methods: A total of 554 pregnant women were selected and their sociodemographic characteristics, clinical data and dietary data were collected. Dietary data was investigated by a validated semi-quantitative food frequency questionnaire (FFQ). We applied random forest mean decrease impurity for feature selection and the models are built using Logistic Regression, XGBoost, and LightGBM algorithms. The prediction performance of different models was compared by Accuracy, Sensitivity, Specificity, Area Under Curve (AUC) and Hosmer-Lemeshow test.
Results: Blood glucose, age, pre-pregnancy body mass index (BMI), triglycerides and high-density lipoprotein cholesterol (HDL) were the top five features according to the feature selection. Among the three algorithms, XGBoost performed best with an AUC of 0.788, LightGBM came second (AUC = 0.749), and Logistic Regression performed the worst (AUC = 0.712). In addition, XGBoost and LightGBM both achieved a fairly good performance when dietary information was included, surpassing their performance on the non-dietary dataset (0.788 vs. 0.718 in XGBoost; 0.749 vs. 0.726 in LightGBM).
Conclusion: XGBoost and LightGBM algorithms outperform Logistic Regression in predicting GDM among the Chinese pregnant women. In addition, dietary data may have a positive effect on improving model performance, which deserves more in-depth investigation with larger sample size.
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Introduction Over the preceding three decades, there has been a fourfold global escalation in the prevalence of diabetes mellitus, culminating in approximately 1 out of 11 adults being diagnosed with this condition, a substantial 90% of whom
