The present umbrella review aims to collate and summarize the findings from previous meta-analyses on the Triglyceride and Glucose (TyG) Index, providing insights to clinicians, researchers, and policymakers regarding the usefulness of this biomarker in various clinical settings. A comprehensive search was conducted in PubMed, Scopus, and Web of Science up to April 14, 2024, without language restrictions. The AMSTAR2 checklist assessed the methodological quality of the included meta-analyses. Statistical analyses were performed using Comprehensive Meta-Analysis (CMA) software. A total of 32 studies were finally included. The results revealed significant associations between the TyG index and various health outcomes. For kidney outcomes, a high TyG index was significantly associated with an increased risk of contrast-induced nephropathy (CIN) (OR = 2.24, 95% CI: 1.82–2.77) and chronic kidney disease (CKD) (RR = 1.46, 95% CI: 1.32–1.63). High TyG index was significantly associated with an increased risk of type 2 diabetes mellitus (T2DM) (RR = 3.53, 95% CI: 2.74–4.54), gestational diabetes mellitus (GDM) (OR = 2.41, 95% CI: 1.48–3.91), and diabetic retinopathy (DR) (OR = 2.34, 95% CI: 1.31–4.19). Regarding metabolic diseases, the TyG index was significantly higher in patients with obstructive sleep apnea (OSA) (SMD = 0.86, 95% CI: 0.57–1.15), metabolic syndrome (MD = 0.83, 95% CI: 0.74–0.93), and non-alcoholic fatty liver disease (NAFLD) (OR = 2.36, 95% CI: 1.88–2.97) compared to those without these conditions. In cerebrovascular diseases, a higher TyG index was significantly associated with an increased risk of dementia (OR = 1.14, 95% CI: 1.12–1.16), cognitive impairment (OR = 2.31, 95% CI: 1.38–3.86), and ischemic stroke (OR = 1.37, 95% CI: 1.22–1.54). For cardiovascular outcomes, the TyG index showed significant associations with an increased risk of heart failure (HF) (HR = 1.21, 95% CI: 1.12–1.30), atrial fibrillation (AF) (SMD = 1.22, 95% CI: 0.57–1.87), and hypertension (HTN) (RR = 1.52, 95% CI: 1.25–1.85). The TyG index is a promising biomarker for screening and predicting various medical conditions, particularly those related to insulin resistance and metabolic disorders. However, the heterogeneity and methodological quality of the included studies suggest the need for further high-quality research to confirm these findings and refine the clinical utility of the TyG index.

Background: Contrast-induced nephropathy (CIN) is a form of acute kidney injury (AKI) occurring in patients undergoing cardiac catheterization, such as coronary angiography (CAG) or percutaneous coronary intervention (PCI). Although the conventional criterion for CIN detection involves a rise in creatinine levels within 72 h after contrast media injection, several limitations exist in this definition. Up to now, various meta-analyses have been undertaken to assess the accuracy of different biomarkers of CIN prediction. However, the existing body of research lacks a cohesive overview. To address this gap, a comprehensive umbrella review was necessary to consolidate and summarize the outcomes of prior meta-analyses. This umbrella study aimed to offer a current, evidence-based understanding of the prognostic value of biomarkers in predicting CIN. Methods: A systematic search of international databases, including PubMed, Scopus, and Web of Science, from inception to December 12, 2023, was conducted to identify meta-analyses assessing biomarkers for CIN prediction. Our own meta-analysis was performed by extracting data from the included studies. Sensitivity, specificity, positive likelihood ratio, and negative likelihood ratio were assessed using Meta-Disc and CMA softwares. Results: Twelve studies were ultimately included in the umbrella review. The results revealed that neutrophil gelatinase-associated lipocalin (NGAL) exhibited the highest area under the curve (AUC), followed by cystatin-C, urinary kidney injury molecule-1 (uKIM-1), and brain natriuretic peptide (BNP) with AUCs of 0.91, 0.89, 0.85, and 0.80, respectively. NGAL also demonstrated the highest positive likelihood ratio [effect size (ES): 6.02, 95% CI 3.86–9.40], followed by cystatin-C, uKIM-1, and BNP [ES: 4.35 (95% CI 2.85–6.65), 3.58 (95% CI 2.75–4.66), and 2.85 (95% CI 2.13–3.82), respectively]. uKIM-1 and cystatin-C had the lowest negative likelihood ratio, followed by NGAL and BNP [ES: 0.25 (95% CI 0.17–0.37), ES: 0.25 (95% CI 0.13–0.50), ES: 0.26 (95% CI 0.17–0.41), and ES: 0.39 (0.28–0.53) respectively]. NGAL emerged as the biomarker with the highest diagnostic odds ratio for CIN, followed by cystatin-C, uKIM-1, BNP, gamma-glutamyl transferase, hypoalbuminemia, contrast media volume to creatinine clearance ratio, preprocedural hyperglycemia, red cell distribution width (RDW), hyperuricemia, neutrophil-to-lymphocyte ratio, C-reactive protein (CRP), high-sensitivity CRP, and low hematocrit (P < 0.05). Conclusion: NGAL demonstrated superior diagnostic performance, exhibiting the highest AUC, positive likelihood ratio, and diagnostic odds ratio among biomarkers for CIN, followed by cystatin-C, and uKIM-1. These findings underscore the potential clinical utility of NGAL, cystatin-C and uKIM-1 in predicting and assessing CIN. Graphical

Background: This study explores how weight loss impacts thyroid hormone levels, particularly free T3 (FT3) and FT4, in obese individuals, given the key role these hormones play in regulating metabolism and gene expression. Methods: A comprehensive literature search in PubMed, ISI/Web of Science, and Scopus databases identified studies examining the correlation between weight, body mass index (BMI), and thyroid hormone levels. Results: The analysis revealed that BMI reduction significantly decreases FT3 levels [odds ratio (OR) = 2.31, 95% confidence interval (CI): 1.73–3.10, P < 0.01] and increases FT4 levels (OR = 0.68, 95% CI: 0.47–0.98, P = 0.04). Weight loss results in a significant reduction in FT3 (OR = 2.47, 95% CI: 1.68–3.64, P < 0.01) and thyroid-stimulating hormone (TSH) (OR = 1.76, 95% CI: 1.15–2.69, P < 0.01), as well as a significant increase in FT4 (OR = 0.69, 95% CI: 0.55–0.88, P < 0.01). Subgroup analysis indicated that changes in thyroid hormone levels were more pronounced in obese individuals who underwent diet calorie restriction compared to bariatric surgery. Conclusion: The study results indicate that weight reduction leads to a decrease in TSH and FT3 levels and an increase in FT4 levels. Calorie restriction through diet has a more pronounced effect on thyroid function compared to bariatric surgery. Clinicians should approach elevated TSH levels in obese patients with caution, emphasizing weight management strategies as the primary therapeutic intervention before resorting to pharmacological treatments.

Background: The introduction of ChatGPT by OpenAI has garnered significant attention. Among its capabilities, paraphrasing stands out. Objective: This study aims to investigate the satisfactory levels of plagiarism in the paraphrased text produced by this chatbot. Methods: Three texts of varying lengths were presented to ChatGPT. ChatGPT was then instructed to paraphrase the provided texts using five different prompts. In the subsequent stage of the study, the texts were divided into separate paragraphs, and ChatGPT was requested to paraphrase each paragraph individually. Lastly, in the third stage, ChatGPT was asked to paraphrase the texts it had previously generated. Results: The average plagiarism rate in the texts generated by ChatGPT was 45% (SD 10%). ChatGPT exhibited a substantial reduction in plagiarism for the provided texts (mean difference −0.51, 95% CI −0.54 to −0.48; P<.001). Furthermore, when comparing the second attempt with the initial attempt, a significant decrease in the plagiarism rate was observed (mean difference −0.06, 95% CI −0.08 to −0.03; P<.001). The number of paragraphs in the texts demonstrated a noteworthy association with the percentage of plagiarism, with texts consisting of a single paragraph exhibiting the lowest plagiarism rate (P<.001). Conclusion: Although ChatGPT demonstrates a notable reduction of plagiarism within texts, the existing levels of plagiarism remain relatively high. This underscores a crucial caution for researchers when incorporating this chatbot into their work.