Coronary heart disease (CHD) is a major mortality risk factor in patients with diabetes. LDL cholesterol (LDL-C) is a major risk factor for the development of atherosclerosis. There is one apolipoprotein B (ApoB) molecule in each LDL particle. We aimed to evaluate the predictive value of the LDL-C/ApoB ratio for CHD in patients with type 2 diabetes (T2D).
 
  In this case-cohort study (apo)lipoproteins and glycemic indices were measured in 1058 individuals with T2D from February 2002 to March 2019, with a median duration of follow up of 10 years.  https://www.selleckchem.com/products/Furosemide(Lasix).html  Of 1058 patients with T2D, coronary heart disease occurred in 242 patients. Increased waist circumference, waist-to-hip ratio, and hemoglobin A1c, low-density lipoprotein cholesterol (LDL-C)/Apolipoprotein B (ApoB) ratio, presence of hypertension and metabolic syndrome, and insulin and statin use were more prevalent among patients with CHD (P<0.001). Logistic regression analysis showed that an LDL-C/ApoB ratio equal or lower than 1.2 could predict CHD independent of ASCVD risk score [adjusted OR1.841, CI1.257-2.698, P<0.001] when adjusted for multiple confounders. The atherogenic index of plasma (AIP) did not predict CHD.
 
  This study showed that LDL-C/ApoB ratio, but not the atherogenic index of plasma, may be considered as an indicator of CHD independent of the ASCVD risk score in patients with T2D. This finding merits further clarification to optimize preventive strategies for CHD.
 This study showed that LDL-C/ApoB ratio, but not the atherogenic index of plasma, may be considered as an indicator of CHD independent of the ASCVD risk score in patients with T2D. This finding merits further clarification to optimize preventive strategies for CHD.
  Diabetes mellitus (DM) is a frequent comorbidity in ST-elevation-myocardial infarction (STEMI) patients and carries a higher risk of in-hospital mortality. We recently demonstrated that the higher in-hospital mortality of STEMI patients with DM, when compared to that of patients without DM, is mainly associated with their more frequent cardiac and renal dysfunction. These exploratory results prompted us to hypothesize that this higher risk in DM patients is mediated by their lower cardio-renal functional reserve.
 
  We included 5152 STEMI patients treated with primary angioplasty. By using an advanced statistical methodology (path analysis), able to clarify the putative causal paths between variables of interest, we reported that the higher in-hospital mortality of STEMI patients with DM is possibly caused by its adverse impact on cardio-renal function.
 
  This statistical approach allows to reinforce the well-known notion that DM is associated with an increased in-hospital mortality risk in STEMI and sheds lights on the causal relationship among DM, cardio-renal dysfunction, and higher in-hospital mortality. Whether the mortality gap between DM and non-DM patients with STEMI can be reduced by pharmacological strategies combining cardio-renal protective effects is an intriguing question that deserves an answer in the future.
 This statistical approach allows to reinforce the well-known notion that DM is associated with an increased in-hospital mortality risk in STEMI and sheds lights on the causal relationship among DM, cardio-renal dysfunction, and higher in-hospital mortality. Whether the mortality gap between DM and non-DM patients with STEMI can be reduced by pharmacological strategies combining cardio-renal protective effects is an intriguing question that deserves an answer in the future.
  Host-microbiota interactions involving metabolic pathways have been linked to the pathogenesis of atherosclerotic disease and type 2 diabetes. As stable coronary artery disease (SCAD) patients combined with type 2 diabetes have significantly increased risk for cardiac event, we focused on elucidating the role of microbiota affecting cardiometabolic disease development.
 
  We used multi-omics analyses (metagenomics and metabolomics) of fecal and serum samples from a prospective cohort including stable coronary artery disease combined with diabetes mellitus (SCAD+T2DM, n=38), SCAD (n=71), and healthy control (HC, n=55). We linked microbiome features to disease severity in a three-pronged association analysis and identified prognostic bacterial biomarkers. We identified that bacterial and metabolic signatures varied significantly between SCAD and SCAD+T2DM groups. SCAD+T2DM individuals were characterized by increased levels of aromatic amino acids and carbohydrates, which correlate with a gut microbiome with enriched biosynthetic potential. Our study also addressed how metformin may confound gut dysbiosis and increase the potential for nitrogen metabolism. In addition, we found that specific bacterial taxa Ruminococcus torques [HR 2.363 (08-4.56), P=0.03] was predictive of cardiac survival outcomes.
 
  Overall, our study identified relationships between features of the gut microbiota (GM) and circulating metabolites, providing a new direction for future studies aiming to understand the host-GM interplay in atherosclerotic cardiovascular pathogenesis.
 Overall, our study identified relationships between features of the gut microbiota (GM) and circulating metabolites, providing a new direction for future studies aiming to understand the host-GM interplay in atherosclerotic cardiovascular pathogenesis.
  Seasonal variations in several risk factors for cardiovascular events (CVD) were described. Here, we evaluate the impact of seasonal variations in blood pressure (BP), lipid profile and glycemic control on estimated CVD risk in patients with type 2 diabetes (T2D).
 
  Retrospective monocentric study of patients with T2D who were visited at least once in the winter period and once in the summer period, less than 8 months apart, for which data related to systolic (S) BP, diastolic (D) BP, body mass index, glycosylated hemoglobin (HbA1c), total cholesterol, HDL cholesterol and smoking habit were available on both occasions. The 10-year CVD risk was calculated using the UKPDS risk engine and the ASCVD risk estimator. As many as 411 patients were included in the study. Significant within-patient differences between summer and winter were found for the absolute risk of events assessed with both calculators (Δs-w UKPDS-CHD -1.33%, Δs-w UKPDS-Stroke -0.84%, Δs-w ASCVD -2.21%). The seasonal change in SBP was the main responsible for the change in risk estimated with both the UKPDS-Stroke (r
  =0.