Advanced lipoprotein parameters could better explain atheromatosis in non-diabetic chronic kidney disease patients.

BACKGROUND: Chronic kidney disease (CKD) patients have a high burden of atheromatous cardiovascular disease (ASCVD) not fully explained by traditional lipid parameters. Lipoprotein composition and subclass particle number information could improve ASCVD risk assessment. The objective of this study is to investigate the association of advanced lipoprotein parameters with the risk of atheromatosis in a subpopulation of the NEFRONA study. METHODS: This was a cross-sectional study in 395 non-diabetic individuals (209 CKD and 186 non-diabetic and non-CKD) without statin therapy. Vascular ultrasound examination assessing 10 territories was combined with advanced lipoprotein testing performed by nuclear magnetic resonance spectroscopy. Logistic regression was used to estimate adjusted odds ratios (ORs) per 1 standard deviation increment. RESULTS: Atheromatosis was more prevalent in CKD patients (33.9% versus 64.6%). After adjusting for age, gender, smoking habit and CKD stage, the amount of triglycerides (TGs) within low-density lipoprotein (LDL) lipoproteins was independently and positively associated with atheromatosis [OR 1.33; 95% confidence interval (CI) 1.03-1.74; P = 0.03]. Similarly, total and medium LDL particles (LDL-Ps) showed a positive association (OR 1.29; 95% CI 1.00-1.68; P = 0.05 and OR 1.34; 95% CI 1.04-1.75; P = 0.03, respectively). TG-loaded medium LDL-Ps were higher in CKD patients compared with controls and showed an adjusted OR of 1.40 (95% CI 1.09-1.82; P = 0.01) in non-diabetic patients (CKD and non-CKD individuals). In contrast, non-diabetic CKD patients showed a similar coefficient but the significance was lost (OR 1.2; 95% CI 0.8-1.7; P = 0.359). CONCLUSIONS: Non-diabetic CKD patients showed a higher amount of TG-loaded medium LDL-Ps compared with controls. These particles were independently associated with atheromatosis in non-diabetic patients.

Spanish People with Type 2 Diabetes Show an Improved Adherence to the Mediterranean Diet.

The aim of this study was to assess the dietary pattern (i.e., Mediterranean Diet (MedDiet) and healthy eating) in people with type 2 diabetes (T2D) compared with those without diabetes. In addition, we explored clinical factors associated with the dietary pattern. This cross-sectional study was performed with a sample of 476 participants (238 with T2D and 238 participants without diabetes, matched for age and sex). The alternate Mediterranean Diet (aMED) score and the alternate Healthy Eating Index (aHEI) were calculated. Statistical analysis included comparison between groups and multivariable models. Participants with T2D showed higher aMED and aHEI scores (mean (SD): 4.3 (1.5) and 43.9 (6.5), respectively) in comparison with the control group (3.5 (1.8) and 39.4 (7.4), respectively; p < 0.001). In addition, a higher proportion of participants with T2D in higher tertiles of aMED (21.8%) and aHEI (39.9%) was observed compared with participants without diabetes (11.3% for the aMED, and 19.3% for the aHEI; p < 0.001). The adjusted multivariable analysis revealed that T2D (p < 0.001), increasing age (p = 0.006 and p = 0.030, respectively), and physical activity (p = 0.009) were positively associated with higher aMED and aHEI scores. Dyslipidemia and female gender were positively associated with aMED and aHEI (p = 0.031 and p < 0.001, respectively). The specific multivariable analysis for the group with T2D yielded a positive association of age (p < 0.001) and dyslipidemia (p = 0.021) with aMED. Regarding the aHEI, only female gender was positively related with this score in diabetes participants (p = 0.025). Participants with T2D showed a higher adherence to the MedDiet and a healthier eating pattern.

Subclinical atherosclerosis burden predicts cardiovascular events in individuals with diabetes and chronic kidney disease.

BACKGROUND: Individuals with diabetes have remarkably high rates of cardiovascular morbidity and mortality. However, the incremental cardiovascular risk in diabetes is heterogeneous and has often been related to renal involvement. The purpose of this study was to analyse the prognostic value of subclinical atherosclerosis in determining the incidence of first cardiovascular events (CVEs) in individuals with diabetes and chronic kidney disease (CKD) compared to CKD individuals without diabetes. METHODS: We included data from individuals with CKD with and without diabetes, free from pre-existing cardiovascular disease, from the NEFRONA cohort. Participants underwent baseline carotid and femoral ultrasound and were followed up for 4 years. All CVEs during follow-up were registered. Bivariate analysis and Fine-Gray competing risk models were used to perform the statistical analysis. RESULTS: During the mean follow-up time of 48 months, a total of 203 CVE was registered. 107 CVE occurred among participants without diabetes (19.58 per 1000 person-years) and 96 CVE occurred among participants with diabetes (44.44 per 1000 person-years). Following the competing risk analysis, the variables predicting CVEs in CKD individuals without diabetes were the number of territories with plaque at baseline (HR 1.862, 95% CI [1.432;2.240]), age (HR 1.026, 95% CI [1.003;1.049]) and serum concentrations of 25-OH vitamin D (HR 0.963, 95% CI [0.933;0.094]). The only variable predicting CVEs among CKD participants with diabetes was the number of territories with plaque at baseline (HR 1.782, 95% CI [1.393, 2.278]). For both models, concordance (C) index yielded was over 0.7. CONCLUSIONS: The burden of subclinical atherosclerosis is the strongest predictor of future CVEs in diabetic individuals with CKD. Early detection of subclinical atherosclerotic burden by multiterritorial vascular ultrasound could improve CVE prediction in this population.

Prevalence and progression of subclinical atherosclerosis in patients with chronic kidney disease and diabetes.

BACKGROUND AND AIMS: Cardiovascular disease is the leading cause of morbidity and mortality in patients with chronic kidney disease (CKD) and diabetes. Traditional cardiovascular risk factors fail to fully account for the increase in cardiovascular risk in these patients. This study aims to analyse the prevalence and progression of subclinical atherosclerosis in CKD patients with and without diabetes. METHODS: We included data from CKD patients with and without diabetes free from previous cardiovascular events from the NEFRONA cohort. Patients underwent baseline and 24-month follow-up carotid and femoral ultrasound examinations. Multivariable models were used to assess the contribution of diabetes to the presence and plaque progression. RESULTS: A total of 419 patients with diabetes and 1129 without diabetes were included. Diabetic patients were older, had higher BMIs, more hypertension and dyslipidaemia. At baseline, the proportion of patients with plaque was higher among diabetic patients (81.4% vs. 64.1%, p < 0.001). Diabetic patients more frequently had more than two vascular territories with plaque (64.4% vs. 48.4%, p < 0.001). Multivariable analysis indicated that plaque at baseline was significantly associated with age, gender, smoking and renal replacement therapy (RRT) in the non-diabetic patients, but only with age and male gender in diabetic patients. Plaque progression was significantly associated with age, number of territories with basal plaque, smoking and RRT in both groups. CONCLUSIONS: Subclinical atherosclerosis is more prevalent, carries a higher plaque burden and is more rapidly progressive in renal patients with diabetes. In these patients, diabetes outweighs other described risk factors associated with the presence of subclinical atherosclerosis.

Bayesian joint modeling for assessing the progression of chronic kidney disease in children.

Joint models are rich and flexible models for analyzing longitudinal data with nonignorable missing data mechanisms. This article proposes a Bayesian random-effects joint model to assess the evolution of a longitudinal process in terms of a linear mixed-effects model that accounts for heterogeneity between the subjects, serial correlation, and measurement error. Dropout is modeled in terms of a survival model with competing risks and left truncation. The model is applied to data coming from ReVaPIR, a project involving children with chronic kidney disease whose evolution is mainly assessed through longitudinal measurements of glomerular filtration rate.

Antimicrobial resistance in more than 100,000 Escherichia coli isolates according to culture site and patient age, gender, and location.

Escherichia coli and the antimicrobial pressure exerted on this microorganism can be modulated by factors dependent on the host. In this paper, we describe the distribution of antimicrobial resistance to amikacin, tobramycin, ampicillin, amoxicillin clavulanate, cefuroxime, cefoxitin, cefotaxime, imipenem, ciprofloxacin, fosfomycin, nitrofurantoin, and trimetoprim-sulfametoxazole in more than 100,000 E. coli isolates according to culture site and patient age, gender, and location. Bayesian inference was planned in all statistical analysis, and Markov chain Monte Carlo simulation was employed to estimate the model parameters. Our findings show the existence of a marked difference in the susceptibility to several antimicrobial agents depending on from where E. coli was isolated, with higher levels of resistance in isolates from medical devices, the respiratory system, and the skin and soft tissues; a higher resistance percentage in men than in women; and the existence of a clear difference in antimicrobial resistance with an age influence that cannot be explained merely by means of an increase of resistance after exposure to antimicrobials. Both men and women show increases in resistance with age, but while women show constant levels of resistance or slight increases during childbearing age and greater increases in the premenopausal age, men show a marked increase in resistance in the pubertal age. In conclusion, an overwhelming amount of data reveals the great adaptation capacity of E. coli and its close interaction with the host. Sex, age, and the origin of infection are determining factors with the ability to modulate antimicrobial resistances.