Lipoprotein(a) and carotid intima-media thickness in children with familial hypercholesterolaemia in the Netherlands: a 20-year follow-up study.

BACKGROUND: Elevated lipoprotein(a) and familial hypercholesterolaemia are both independent risk conditions for cardiovascular disease. Although signs of atherosclerosis can be observed in children with familial hypercholesterolaemia, it is unknown whether elevated lipoprotein(a) is an additional risk factor for atherosclerosis in these young patients. Therefore, we aimed to assess the contribution of lipoprotein(a) concentrations to arterial wall thickening (as measured by carotid intima-media thickness) in children with familial hypercholesterolaemia who were followed up into adulthood. METHODS: We conducted a 20-year follow-up study of 214 children (aged 8-18 years) with heterozygous familial hypercholesterolaemia who were randomly assigned in a statin trial in Amsterdam (Netherlands) between Dec 7, 1997, and Oct 4, 1999. At baseline, and at 2, 10, and 20 years thereafter, blood samples were taken and carotid intima-media thickness was measured. Linear mixed-effects models were used to evaluate the association between lipoprotein(a) and carotid intima-media thickness during follow-up. We adjusted for sex, age, corrected LDL-cholesterol, statin use, and BMI. FINDINGS: Our study population comprised 200 children who had a carotid intima-media thickness measurement and a measured lipoprotein(a) concentration from at least one visit available. Mean age at baseline was 13·0 years (SD 2·9), 106 (53%) children were male, and 94 (47%) were female. At baseline, median lipoprotein(a) concentration was 18·5 nmol/L (IQR 8·7-35·5) and mean carotid intima-media thickness was 0·4465 mm (SD 0·0496). During follow-up, higher lipoprotein(a) concentrations contributed significantly to progression of carotid intima-media thickness (ß adjusted 0·0073 mm per 50 nmol/L increase in lipoprotein(a) [95% CI 0·0013-0·0132]; p=0·017). INTERPRETATION: Our findings suggest that lipoprotein(a) concentrations contribute significantly to arterial wall thickening in children with familial hypercholesterolaemia who were followed-up until adulthood, suggesting that lipoprotein(a) is an independent and additional risk factor for early atherosclerosis in those already at increased risk. Lipoprotein(a) measurement in young patients with familial hypercholesterolaemia is crucial to identify those at potentially highest risk for cardiovascular disease. FUNDING: Silence Therapeutics.

BAHAMA: A Bayesian Hierarchical Model for the Detection of MedDRA®-Coded Adverse Events in Randomized Controlled Trials.

INTRODUCTION: Patients participating in randomized controlled trials (RCTs) are susceptible to a wide range of different adverse events (AE) during the RCT. MedDRA® is a hierarchical standardization terminology to structure the AEs reported in an RCT. The lowest level in the MedDRA hierarchy is a single medical event, and every higher level is the aggregation of the lower levels. METHOD: We propose a multi-stage Bayesian hierarchical Poisson model for estimating MedDRA-coded AE rate ratios (RRs). To deal with rare AEs, we introduce data aggregation at a higher level within the MedDRA structure and based on thresholds on incidence and MedDRA structure. RESULTS: With simulations, we showed the effects of this data aggregation process and the method's performance. Furthermore, an application to a real example is provided and compared with other methods. CONCLUSION: We showed the benefit of using the full MedDRA structure and using aggregated data. The proposed model, as well as the pre-processing, is implemented in an R-package: BAHAMA.

A Bayesian Negative Binomial Hierarchical Model for Identifying Diet-Gut Microbiome Associations.

The human gut microbiota composition plays an important role in human health. Long-term diet intervention may shape human gut microbiome. Therefore, many studies focus on discovering links between long-term diets and gut microbiota composition. This study aimed to incorporate the phylogenetic relationships between the operational taxonomic units (OTUs) into the diet-microbe association analysis, using a Bayesian hierarchical negative binomial (NB) model. We regularized the dispersion parameter of the negative binomial distribution by assuming a mean-dispersion association. A simulation study showed that, if over-dispersion is present in the microbiome data, our approach performed better in terms of mean squared error (MSE) of the slope-estimates compared to the standard NB regression model or a Bayesian hierarchical NB model without including the phylogenetic relationships. Data of the Healthy Life in an Urban Setting (HELIUS) study showed that for some phylogenetic families the (posterior) variances of the slope-estimates were decreasing when including the phylogenetic relationships into the analyses. In contrast, when OTUs of the same family were not similarly affected by the food item, some bias was introduced, leading to larger (posterior) variances of the slope-estimates. Overall, the Bayesian hierarchical NB model, with a dependency between the mean and dispersion parameters, proved to be a robust method for analyzing diet-microbe associations.