👤 Carlos Celis-Morales

This study aims to explore whether conventional and emerging biomarkers could improve risk discrimination and calibration in the secondary prevention of recurrent atherosclerotic cardiovascular disease (ASCVD), based on a model using predictors from SMART2 (Secondary Manifestations of ARTerial Disease). In a cohort of 20 658 UK Biobank participants with medical history of ASCVD, we analysed any improvement in C indices and net reclassification index (NRI) for future ASCVD events, following addition of lipoprotein A (LP-a), apolipoprotein B, Cystatin C, Hemoglobin A1c (HbA1c), gamma-glutamyl transferase (GGT), aspartate aminotransferase (AST), alanine aminotransferase, and alkaline phosphatase (ALP), to a model with predictors used in SMART2 for the outcome of recurrent major cardiovascular event. We also examined any improvement in C indices and NRIs replacing creatinine-based estimated glomerular filtration rate (eGFR) with Cystatin C-based estimates. Calibration plots between different models were also compared. Compared with the baseline model (C index = 0.663), modest increments in C indices were observed when adding HbA1c (ΔC = 0.0064, P < 0.001), Cystatin C (ΔC = 0.0037, P < 0.001), GGT (ΔC = 0.0023, P < 0.001), AST (ΔC = 0.0007, P < 0.005) or ALP (ΔC = 0.0010, P < 0.001) or replacing eGFRCr with eGFRCysC (ΔC = 0.0036, P < 0.001) or eGFRCr-CysC (ΔC = 0.00336, P < 0.001). Similarly, the strongest improvements in NRI were observed with the addition of HbA1c (NRI = 0.014) or Cystatin C (NRI = 0.006) or replacing eGFRCr with eGFRCr-CysC (NRI = 0.001) or eGFRCysC (NRI = 0.002). There was no evidence that adding biomarkers modified calibration. Adding several biomarkers, most notably Cystatin C and HbA1c, but not LP-a, in a model using SMART2 predictors modestly improved discrimination. Show less

The melanocortin receptor 4 (MC4R) participates in the control of appetite at the level of the central nervous system, through the leptin-melanocortin pathway. An association between different polymorphisms of the MC4R gene and obesity has been reported. However, there are few studies of the rs483145 single nucleotide polymorphism (SNP) of this gene. To investigate its prevalence and association with adiposity markers in Chilean adults. The prevalence of SNP rs483145, of the MC4R gene, was determined in 259 participants of the GENADIO study (genes, environment, diabetes and obesity) by means of real-time polymerase chain reaction (PCR). The association between the risk allele of MC4R (A) and adiposity markers (body weight, body mass index, fat mass percentage, hip circumference, waist circumference, waist-to-hip ratio) was performed by linear regression analysis and adjusted for confusion variables (socio-demographic and physic activity) using three statistical models. It was determined that the prevalence of the risk allele (A) of the SNP rs483145 of the MC4R gene is 24.5% in the Chilean adult population included in this study, without finding an association with any of the adiposity markers studied, both in adjusted and unadjusted models. The presence of the risk allele of SNP rs483145 of the MC4R gene is not associated with adiposity markers in the Chilean adult population studied. New studies with a bigger sample size will be necessary to confirm these results. Show less

A high intake of n-3 PUFA provides health benefits via changes in the n-6/n-3 ratio in blood. In addition to such dietary PUFAs, variants in the fatty acid desaturase 1 (FADS1) gene are also associated with altered PUFA profiles. We used mathematical modeling to predict levels of PUFA in whole blood, based on multiple hypothesis testing and bootstrapped LASSO selected food items, anthropometric and lifestyle factors, and the rs174546 genotypes in FADS1 from 1607 participants (Food4Me Study). The models were developed using data from the first reported time point (training set) and their predictive power was evaluated using data from the last reported time point (test set). Among other food items, fish, pizza, chicken, and cereals were identified as being associated with the PUFA profiles. Using these food items and the rs174546 genotypes as predictors, models explained 26-43% of the variability in PUFA concentrations in the training set and 22-33% in the test set. Selecting food items using multiple hypothesis testing is a valuable contribution to determine predictors, as our models' predictive power is higher compared to analogue studies. As unique feature, we additionally confirmed our models' power based on a test set. Show less