The Triglycerides and Glucose Index Is an Independent Risk Factor for Acute Respiratory Distress Syndrome in Patients with COVID-19

Triglyceride glucose (TyG) index: A promising biomarker for diagnosis and treatment of different diseases

The Triglyceride-glucose index (TyG index) is a comprehensive statistical measure that incorporates fasting triglyceride and fasting glucose levels. Research has demonstrated that it can serve as an effective alternative biomarker for insulin resistance (IR) due to its high sensitivity and specificity. The TyG index is straightforward to compute and imposes fewer time and cost constraints, rendering it suitable for large populations and advantageous for use in various applications, clinical settings, and epidemiological investigations. Numerous high-quality clinical studies have underscored the significance of the TyG index in diverse medical conditions. This review provides a synthesis of the association between the TyG index and diseases such as diabetes, cardiovascular diseases, cerebrovascular diseases, fatty liver, kidney diseases, and reproductive system diseases. Furthermore, the TyG index has exhibited predictive capabilities for identifying IR in children and adolescents. Through a systematic review of pertinent clinical trials, this paper elucidates the correlation between the TyG index and various diseases. The findings presented herein suggest that the TyG index holds promise as a valuable and practical indicator for different medical conditions, prompting a reevaluation of conventional disease risk assessment paradigms and highlighting the intricate interplay of metabolic parameters with diverse diseases. By leveraging insights from the TyG index, tailored disease risk management strategies can be developed to offer a fresh perspective and guidance for clinical interventions.

Early predictive values of clinical assessments for ARDS mortality: a machine-learning approach

Acute respiratory distress syndrome (ARDS) is a devastating critical care syndrome with significant morbidity and mortality. The objective of this study was to evaluate the predictive values of dynamic clinical indices by developing machine-learning (ML) models for early and accurate clinical assessment of the disease prognosis of ARDS. We conducted a retrospective observational study by applying dynamic clinical data collected in the ARDSNet FACTT Trial (n = 1000) to ML-based algorithms for predicting mortality. In order to compare the significance of clinical features dynamically, we further applied the random forest (RF) model to nine selected clinical parameters acquired at baseline and day 3 independently. An RF model trained using clinical data collected at day 3 showed improved performance and prognostication efficacy (area under the curve [AUC]: 0.84, 95% CI: 0.78-0.89) compared to baseline with an AUC value of 0.72 (95% CI: 0.65-0.78). Mean airway pressure (MAP), bicarbonate, age, platelet count, albumin, heart rate, and glucose were the most significant clinical indicators associated with mortality at day 3. Thus, clinical features collected early (day 3) improved performance of integrative ML models with better prognostication for mortality. Among these, MAP represented the most important feature for ARDS patients' early risk stratification.

The triglyceride-glucose index is a predictor of major adverse cardiovascular events in patients with coronary artery disease and psoriasis: a retrospective cohort study

BACKGROUND

The association between the triglyceride-glucose (TyG) index and clinical outcomes in patients with both coronary artery disease (CAD) and psoriasis is unclear. This study investigated the association between the TyG index and major adverse cardiovascular events (MACE) in patients with both CAD and psoriasis.

METHODS

This retrospective cohort study included patients diagnosed with both CAD and psoriasis who underwent coronary angiography at the Fuwai Hospital, Beijing, China, between January 2017 and May 2022. The study endpoint was the occurrence of MACE or end of follow-up time. Multivariate Cox proportional analysis and restricted cubic splines (RCS) were used to determine the association between the TyG index and MACE. Receiver operating characteristic (ROC) curves were used to determine the optimal threshold value of the TyG index for predicting MACE.

RESULTS

This study enrolled 293 patients with both CAD and psoriasis, including 258 (88.1%) males with a mean age of 58.89 ± 9.61 years. Patients were divided into four groups based on the TyG quartiles: Q1 (N = 74), Q2 (N = 73), Q3 (N = 73), and Q4 (N = 73). After adjusting for the potential confounders, the TyG index was independently associated with MACE, both as a continuous variable (HR = 1.53, 95% CI = 1.03-2.28, P = 0.035) and as a categorical variable (Q1: reference; Q2: HR = 1.85, 95% CI = 0.88-3.87, P = 0.105; Q3: HR = 2.39, 95% CI = 1.14-5.00, P = 0.021; Q4: HR = 2.19, 95% CI = 1.001-4.81, P = 0.0497; P for trend = 0.039). RCS analysis showed an linear association between the TyG index and MACE (P-overall = 0.027, P-non-linear = 0.589). ROC curve analysis showed that the TyG index of ≥ 8.73 was the optimal threshold value (area under the ROC curve = 0.60, 95% CI 0.53-0.67). TyG index ≥ 8.73 was significantly associated with MACE (HR = 2.10, 95% CI = 1.32-3.34, P = 0.002). After adjustment for confounders, the TyG index showed independent association with MACE (HR = 2.00, 95% CI = 1.17-3.42, P = 0.011).

CONCLUSIONS

The TyG index showed a positive linear correlation with MACE in patients with both CAD and psoriasis. The TyG index of ≥ 8.73 might be the optimal threshold for predicting MACE.

The metabolic footprint of Vero E6 cells highlights the key metabolic routes associated with SARS-CoV-2 infection and response to drug combinations

SARS-CoV-2 burdens healthcare systems worldwide, yet specific drug-based treatments are still unavailable. Understanding the effects of SARS-CoV-2 on host molecular pathways is critical for providing full descriptions and optimizing therapeutic targets. The present study used Nuclear Magnetic Resonance-based metabolic footprinting to characterize the secreted cellular metabolite levels (exometabolomes) of Vero E6 cells in response to SARS-CoV-2 infection and to two candidate drugs (Remdesivir, RDV, and Azithromycin, AZI), either alone or in combination. SARS-CoV-2 infection appears to force VE6 cells to have increased glucose concentrations from extra-cellular medium and altered energetic metabolism. RDV and AZI, either alone or in combination, can modify the glycolic-gluconeogenesis pathway in the host cell, thus impairing the mitochondrial oxidative damage caused by the SARS-CoV-2 in the primary phase. RDV treatment appears to be associated with a metabolic shift toward the TCA cycle. Our findings reveal a metabolic reprogramming produced by studied pharmacological treatments that protects host cells against virus-induced metabolic damage, with an emphasis on the glycolytic-gluconeogenetic pathway. These findings may help researchers better understand the relevant biological mechanisms involved in viral infection, as well as the creation of mechanistic hypotheses for such candidate drugs, thereby opening up new possibilities for SARS-CoV-2 pharmacological therapy.