Aims/Introduction: The triglyceride-glucose (TyG) index has been proposed as a reliable and simple marker of insulin resistance. We investigated the association between TyG index and diabetic nephropathy (DN) in patients with type 2 diabetes. Materials and Methods: A consecutive case series of 682 adult patients with type 2 diabetes hospitalized in the Department of Endocrinology at the Tongji Hospital (Wuhan, Hubei, China) from January 2007 to December 2009 was included in this cross-sectional analysis. Receiver operating characteristics curve analysis, correlation analysis and multiple logistic regression analysis were carried out. Results: A total of 232 (34.0%) participants were identified with DN. Compared with the non-DN group, the DN group had longer disease duration, and higher bodyweight, systolic blood pressure, diastolic blood pressure, glycated hemoglobin, triglycerides, total cholesterol, serum uric acid, 24 h-urinary albumin, TyG index and homeostasis model assessment 2 estimates for insulin resistance (HOMA2-IR; P < 0.05 for each). The TyG index with an optimal cutoff point >9.66 showed a higher area under the receiver operating characteristic curve of 0.67 (P = 0.002) than HOMA2-IR (area under the curve 0.61, P = 0.029) on receiver operating characteristic curve analysis for DN identification. Additionally, the TyG index positively correlated with the levels of metabolic indicators (bodyweight, glycated hemoglobin, triglycerides, total cholesterol, serum uric acid, fasting glucose and HOMA2-IR) and natural logarithmic 24 h-urinary albumin (P < 0.05 for each), but not natural logarithm of estimated glomerular filtration rate. On multiple regression analysis, an increased TyG index was shown to be an independent risk factor (odds ratio 1.91, P = 0.001) for DN. Conclusions: The TyG index was independently associated with DN in patients with type 2 diabetes, and was a better marker than HOMA2-IR for identification of DN in type 2 diabetes patients.

Background Acute myocardial injury and heart failure characterized by elevated cardiac troponin and decreased heart pump function are significant clinical features and prognostic factors of coronavirus disease-19 (COVID-19). Triglyceride to high-density lipoprotein cholesterol (TG/HDL-C) ratio is an indicator of insulin resistance. This study aimed to explore the association of the TG/HDL-C ratio with cardiovascular risk and prognosis in COVID-19. Methods Ninety-eight laboratory-confirmed patients with COVID-19 admitted in a tertiary teaching hospital in Wuhan, China, were enrolled in this retrospective study. Regression models were used to investigate the association between TG/HDL-C ratio with myocardial injury, heart failure, severity, and mortality in COVID-19. Results Among the 98 patients, the mean age was 63.9±1.4 years, and male sex (58, 59%) was predominant. Forty-six patients (47%) were admitted to the intensive care unit (ICU), 32 (33%) and 46 (47%) patients suffered from myocardial injury and heart failure, respectively, and 36 (37%) patients died. The TG/HDL-C ratio was increased in patients with myocardial injury, heart failure, severe illness, and fatal outcome ( P <0.05 for each). Baseline TG/HDL-C ratio significantly correlated with log transformed levels of plasma high-sensitivity cardiac troponin I (r=0.251, P =0.018), N-terminal brain natriuretic propeptide (r=0.274, P =0.008), glycated hemoglobin (r=0.239, P =0.038), and interleukin-6 (r=0.218, P =0.042). Multivariate logistic regression analysis showed that an increased TG/HDL-C ratio was independently associated with the risk of myocardial injury [odds ratio (OR)=2.73; P =0.013], heart failure (OR=2.64; P =0.019), disease severity (OR=3.01; P =0.032), and fatal outcome (OR=2.97; P =0.014). Conclusion Increased TG/HDL-C ratio was independently associated with myocardial injury, heart failure, disease severity, and mortality in patients with COVID-19, and it may be a useful marker for early identification of patients with high risk and poor outcome.

Aims/Introduction This study aimed to explore the association between glycemic control before admission with severity and mortality of coronavirus disease 2019, and tried to reveal the mechanism. Materials and Methods A total of 77 inpatients were grouped into sufficient control group (glycated hemoglobin [HbA1c] <6.5%, n = 49) and insufficient control group (HbA1c ≥6.5%, n = 28). Regression models were used to analyze the clinical data. Results Compared with patients with HbA1c <6.5, patients with HbA1c ≥6.5 showed higher heart rate (101 vs 89 b.p.m., P = 0.012), lower percutaneous oxygen saturation (93 vs 97%, P = 0.001), higher levels of multiple indicators of inflammation, such as white blood cell count (7.9 vs 5.9 × 10 9 /L, P = 0.019), neutrophil count (6.5 vs 4.1 × 10 9 /L, P = 0.001), high‐sensitivity C‐reactive protein (52 vs 30 mg/L, P = 0.025) and serum ferritin (1,287 vs 716 μg/L, P = 0.023), as well as lower levels of lymphocyte count (0.7 vs 0.8 × 10 9 /L, P = 0.049) at hospital admission. Thus, patients with HbA1c ≥6.5 were more likely to develop secondary respiratory infections (25 [89%] vs 33 [67%], P = 0.032) and acute respiratory distress syndrome (17 [61%] vs 14 [29%], P = 0.006) than patients with HbA1c <6.5, resulting in a higher proportion of critically ill patients (19 [68%] vs 18 [37%], P = 0.009) and non‐survivors (13 [46%] vs 11 [22%], P = 0.029). After adjustment for potential risk factors, HbA1c was independently associated with in‐hospital death. Conclusion HbA1c was an independent risk factor for poor outcomes in coronavirus disease 2019 patients. Severe pulmonary infection and consequent acute respiratory distress syndrome might be the primary causes of death in insufficient glycemic control patients.

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Coronavirus disease 2019 (COVID-19) has been a rampant worldwide health threat and we aimed to develop a model for early prediction of disease progression. This retrospective study included 124 adult inpatients with COVID-19 who presented with severe illness at admission and had a definite outcome (recovered or progressed to critical illness) during February 2020. Eighty-four patients were used as training cohort and 40 patients as validation cohort. Logistic regression analysis and receiver operating characteristic curve (ROC) analysis were used to develop and evaluate the prognostic prediction model. In the training cohort, the mean age was 63.4 ± 1.5 years, and male patients (48, 57%) were predominant. Forty-three (52%) recovered, and 41 (49%) progressed to critical. Decreased lymphocyte count (LC, odds ratio [OR] = 4.40, P = .026), elevated lactate dehydrogenase levels (LDH, OR = 4.24, P = .030), and high-sensitivity C-reactive protein (hsCRP, OR = 1.01, P = .025) at admission were independently associated with higher odds of deteriorated outcome. Accordingly, we developed a predictive model for disease progression based on the levels of the 3 risk factors (LC, LDH, and hsCRP) with a satisfactory performance in ROC analysis (area under the ROC curve [AUC] = 0.88, P < .001) and the best cut-off value was 0.526 with the sensitivity and specificity of 75.0% and 90.7%, respectively. Then, the model was internally validated by leave-one-out cross-validation with value of AUC 0.85 (P < .001) and externally validated in another validation cohort (26 recovered patients and 14 progressed patients) with AUC 0.84 (P < .001). We identified 3 clinical indicators of risk of progression and developed a severe COVID-19 prognostic prediction model, allowing early identification and intervention of high-risk patients being critically illness.