Impact of 3-year changes in lipid parameters and their ratios on incident type 2 diabetes: Tehran lipid and glucose study

Abstract Background To examine the impact of changes in all lipid measures including total cholesterol (TC), log-transformed triglycerides (Ln-TG), high density lipoprotein cholesterol (HDL-C), low density lipoprotein cholesterol (LDL-C), non-HDL-C, TC/HDL-C and Ln TG/HDL-C, over an approximate 3 year duration, on incident type 2 diabetes (T2DM). Methods A total of 5474 participants, mean age 41.3 years, without prevalent diabetes at baseline or the first follow-up were entered into the study. The association of lipid changes between baseline and the first follow-up i.e., between 1999–2002 and 2002–2005 for those entered in the first phase (n = 4406) and between 2002–2005 and 2005–2008 for participants recruited in the second phase (n = 1068) with incident T2DM over the follow-up period was assessed, using multivariate Cox proportional hazard analysis. Results During a median follow-up of 8.9 years after the second lipid measurements, 577 incident cases of T2DM occurred. After adjustment for a wide variety of confounders and body mass index (BMI) change, each 1-SD increase in TC, Ln-TG, HDL-C, LDL-C, non-HDL-C, Ln-TG/HDL-C and TC/HDL-C was associated with 12, 14, 0.86, 12, 16, 15 and 13% risk for T2DM, respectively (all p-values < 0.05). However, after further adjustment for fasting plasma glucose (FPG) change, the risk disappeared for all lipid measures, excluding HDL-C [hazard ratio (HR): 0.84 (0.76–0.93)], Ln-TG/HDL-C [1.14 (1.04–1.25)] and TC/HDL-C [1.12 (1.04–1.21)]. Conclusions Three year changes in all lipid parameters, after adjustment for known risk factors of T2DM and BMI changes, were associated with incident T2DM. The independent risk of HDL-C and its ratios remained even after adjustment for FPG changes

Outcomes of Patients With Takotsubo Syndrome Compared With Type 1 and Type 2 Myocardial Infarction

Background Takotsubo syndrome (TS) and myocardial infarction (MI) share similar clinical and laboratory characteristics but have important differences in causes, demographics, management, and outcomes. Methods and Results In this observational study, the National Inpatient Sample and National Readmission Database were used to identify patients admitted with TS, type 1 MI, or type 2 MI in the United States between October 1, 2017, and December 31, 2019. We compared patients hospitalized with TS, type 1 MI, and type 2 MI with respect to key features and outcomes. Over the 27‐month study period, 2 035 055 patients with type 1 MI, 639 075 patients with type 2 MI, and 43 335 patients with TS were identified. Cardiac arrest, ventricular fibrillation, and ventricular tachycardia were more prevalent in type 1 MI (4.02%, 3.2%, and 7.2%, respectively) compared with both type 2 MI (2.8%, 0.8%, and 5.4% respectively) and TS (2.7%, 1.8%, and 5.3%, respectively). Risk of mortality was lower in TS compared with both type 1 MI (3.3% versus 7.9%; adjusted odds ratio [OR], 0.3; P<0.001) and type 2 MI (3.3% versus 8.2%; adjusted OR, 0.3; P<0.001). Mortality rate (OR, 1.2; P<0.001) and cardiac‐cause 30‐day readmission rate (adjusted OR, 1.7; P<0.001) were higher in type 1 MI than in type 2 MI. Conclusions Patients with type 1 MI had the highest rates of in‐hospital mortality and cardiac‐cause 30‐day readmission. Risk of all‐cause 30‐day readmission was highest in patients with type 2 MI. The risk of ventricular arrhythmias in patients with TS is lower than in patients with type 1 MI but higher than in patients with type 2 MI

Additional file 1: of Impact of 3-year changes in lipid parameters and their ratios on incident type 2 diabetes: Tehran lipid and glucose study

Table S1. Baseline characteristics in respondents and non-respondents. Table S2. Characteristics of participants at baseline and the first follow-up (DOCX 19 kb

Metadata record for: HIT-COVID, a global database tracking public health interventions to COVID-19

This dataset contains key characteristics about the data described in the Data Descriptor HIT-COVID, a global database tracking public health interventions to COVID-19. Contents: 1. human readable metadata summary table in CSV format 2. machine readable metadata file in JSON forma