Bayesian P-Splines to investigate the impact of covariates on Multiple Sclerosis clinical course

This paper aims at proposing suitable statistical tools to address heterogeneity in repeated measures, within a Multiple Sclerosis (MS) longitudinal study. Indeed, due to unobservable sources of heterogeneity, modelling the effect of covariates on MS severity evolves as a very difficult feature. Bayesian P-Splines are suggested for modelling non linear smooth effects of covariates within generalized additive models. Thus, based on a pooled MS data set, we show how extending Bayesian P-splines to mixed effects models (Lang and Brezger, 2001), represents an attractive statistical approach to investigate the role of prognostic factors in affecting individual change in disability

EFFECTS OF AEROBIC EXERCISE IN THE MANAGEMENT OF ERECTILE DYSFUNCTION: A META ANALYSIS STUDY ON RANDOMIZED CONTROLLED TRIALS

No abstract - Available on PD

Evaluating the Causal Relation of ApoA-IV with Disease-Related Traits - A Bidirectional Two-sample Mendelian Randomization Study

Apolipoprotein A-IV (apoA-IV) has been observed to be associated with lipids, kidney function, adiposity- and diabetes-related parameters. To assess the causal relationship of apoA-IV with these phenotypes, we conducted bidirectional Mendelian randomization (MR) analyses using publicly available summary-level datasets from GWAS consortia on apoA-IV concentrations (n = 13,813), kidney function (estimated glomerular filtration rate (eGFR), n = 133,413), lipid traits (HDL cholesterol, LDL cholesterol, triglycerides, n = 188,577), adiposity-related traits (body-mass-index (n = 322,206), waist-hip-ratio (n = 210,088)) and fasting glucose (n = 133,010). Main analyses consisted in inverse-variance weighted and multivariable MR, whereas MR-Egger regression and weighted median estimation were used as sensitivity analyses. We found that eGFR is likely to be causal on apoA-IV concentrations (53 SNPs; causal effect estimate per 1-SD increase in eGFR = −0.39; 95% CI = [−0.54, −0.24]; p-value = 2.4e-07). Triglyceride concentrations were also causally associated with apoA-IV concentrations (40 SNPs; causal effect estimate per 1-SD increase in triglycerides = −0.06; 95% CI = [−0.08, −0.04]; p-value = 4.8e-07), independently of HDL-C and LDL-C concentrations (causal effect estimate from multivariable MR = −0.06; 95% CI = [−0.10, −0.02]; p-value = 0.0014). Evaluating the inverse direction of causality revealed a possible causal association of apoA-IV on HDL-cholesterol (2 SNPs; causal effect estimate per one percent increase in apoA-IV = −0.40; 95% CI = [−0.60, −0.21]; p-value = 5.5e-05).</p

Use of Analog and Human Insulin in a European Hemodialysis Cohort With Type 2 Diabetes: Associations With Mortality, Hospitalization, MACE, and Hypoglycemia

RATIONALE & OBJECTIVE: Poor glycemic control may contribute to the high mortality rate in patients with type 2 diabetes receiving hemodialysis. Insulin type may influence glycemic control, and its choice may be an opportunity to improve outcomes. This study assessed whether treatment with analog insulin compared with human insulin is associated with different outcomes in people with type 2 diabetes and kidney failure receiving hemodialysis. STUDY DESIGN: Retrospective cohort study. SETTING & PARTICIPANTS: People in the Analyzing Data, Recognizing Excellence and Optimizing Outcomes (AROii) study with kidney failure commencing hemodialysis and type 2 diabetes being treated with insulin within 288 dialysis facilities between 2007 and 2009 across 7 European countries. Study participants were followed for 3 years. People with type 1 diabetes were excluded using an established administrative data algorithm. EXPOSURE: Treatment with an insulin analog or human insulin. OUTCOME: All-cause mortality, major adverse cardiovascular events (MACE), all-cause hospitalization, and confirmed hypoglycemia (blood glucose<3.0mmol/L sampled during hemodialysis). ANALYTICAL APPROACH: Inverse probability weighted Cox proportional hazards models to estimate hazard ratios for analog insulin compared with human insulin. RESULTS: There were 713 insulin analog and 733 human insulin users. Significant variation in insulin type by country was observed. Comparing analog with human insulin at 3 years, the percentage of patients experiencing end points and adjusted hazard ratios (AHR) were 22.0% versus 31.4% (AHR, 0.808 [95% CI, 0.66-0.99], P=0.04) for all-cause mortality, 26.8% versus 35.9% (AHR, 0.817 [95% CI, 0.68-0.98], P=0.03) for MACE, and 58.2% versus 75.0% (AHR, 0.757 [95% CI, 0.67-0.86], P<0.001) for hospitalization. Hypoglycemia was comparable between insulin types at 14.1% versus 15.0% (AHR, 1.169 [95% CI, 0.80-1.72], P=0.4). Consistent strength and direction of the associations were observed across sensitivity analyses. LIMITATIONS: Residual confounding, lack of more detailed glycemia data. CONCLUSIONS: In this large multinational cohort of people with type 2 diabetes and kidney failure receiving maintenance hemodialysis, treatment with analog insulins was associated with better clinical outcomes when compared with human insulin. PLAIN-LANGUAGE SUMMARY: People with diabetes who are receiving dialysis for kidney failure are at high risk of cardiovascular disease and death. This study uses information from 1,446 people with kidney failure from 7 European countries who are receiving dialysis, have type 2 diabetes, and are prescribed either insulin identical to that made in the body (human insulin) or insulins with engineered extra features (insulin analog). After 3 years, fewer participants receiving analog insulins had died, had been admitted to the hospital, or had a cardiovascular event (heart attack, stroke, heart failure, or peripheral vascular disease). These findings suggest that analog insulins should be further explored as a treatment leading to better outcomes for people with diabetes on dialysis

Lipoprotein(a) plasma levels are not associated with incident microvascular complications in type 2 diabetes mellitus

Aims/hypothesis: Microvascular disease in type 2 diabetes is a significant cause of end-stage renal disease, blindness and peripheral neuropathy. The strict control of known risk factors, e.g. lifestyle, hyperglycaemia, hypertension and dyslipidaemia, reduces the incidence of microvascular complications, but a residual risk remains. Lipoprotein (a) [Lp(a)] is a strong risk factor for macrovascular disease in the general population. We hypothesised that plasma Lp(a) levels and the LPA gene SNPs rs10455872 and rs3798220 are associated with the incident development of microvascular complications in type 2 diabetes. Methods: Analyses were performed of data from the DiaGene study, a prospective study for complications of type 2 diabetes, collected in the city of Eindhoven, the Netherlands (n = 1886 individuals with type 2 diabetes, mean follow-up time = 6.97 years). To assess the relationship between plasma Lp(a) levels and the LPA SNPs with each newly developed microvascular complication (retinopathy n = 223, nephropathy n = 246, neuropathy n = 236), Cox proportional hazards models were applied and adjusted for risk factors for microvascular complications (age, sex, mean arterial pressure, non-HDL-cholesterol, HDL-cholesterol, BMI, duration of type 2 diabetes, HbA1c and smoking). Results: No significant associations of Lp(a) plasma levels and the LPA SNPs rs10455872 and rs3798220 with prevalent or incident microvascular complications in type 2 diabetes were found. In line with previous observations the LPA SNPs rs10455872 and rs3798220 did influence the plasma Lp(a) levels. Conclusions/interpretation: Our data show no association between Lp(a) plasma levels and the LPA SNPs with known effect on Lp(a) plasma levels with the development of microvascular complications in type 2 diabetes. This indicates that Lp(a) does not play a major role in the development of microvascular complications. However, larger studies are needed to exclude minimal effects of Lp(a) on the development of microvascular complications

Digging into the extremes: a useful approach for the analysis of rare variants with continuous traits?

The common disease/rare variant hypothesis predicts that rare variants with large effects will have a strong impact on corresponding phenotypes. Therefore it is assumed that rare functional variants are enriched in the extremes of the phenotype distribution. In this analysis of the Genetic Analysis Workshop 17 data set, my aim is to detect genes with rare variants that are associated with quantitative traits using two general approaches: analyzing the association with the complete distribution of values by means of linear regression and using statistical tests based on the tails of the distribution (bottom 10% of values versus top 10%). Three methods are used for this extreme phenotype approach: Fisher’s exact test, weighted-sum method, and beta method. Rare variants were collapsed on the gene level. Linear regression including all values provided the highest power to detect rare variants. Of the three methods used in the extreme phenotype approach, the beta method performed best. Furthermore, the sample size was enriched in this approach by adding additional samples with extreme phenotype values. Doubling the sample size using this approach, which corresponds to only 40% of sample size of the original continuous trait, yielded a comparable or even higher power than linear regression. If samples are selected primarily for sequencing, enriching the analysis by gathering a greater proportion of individuals with extreme values in the phenotype of interest rather than in the general population leads to a higher power to detect rare variants compared to analyzing a population-based sample with equivalent sample size

A genetic algorithm based method for stringent haplotyping of family data

<p>Abstract</p> <p>Background</p> <p>The linkage phase, or haplotype, is an extra level of information that in addition to genotype and pedigree can be useful for reconstructing the inheritance pattern of the alleles in a pedigree, and computing for example Identity By Descent probabilities. If a haplotype is provided, the precision of estimated IBD probabilities increases, as long as the haplotype is estimated without errors. It is therefore important to only use haplotypes that are strongly supported by the available data for IBD estimation, to avoid introducing new errors due to erroneous linkage phases.</p> <p>Results</p> <p>We propose a genetic algorithm based method for haplotype estimation in family data that includes a stringency parameter. This allows the user to decide the error tolerance level when inferring parental origin of the alleles. This is a novel feature compared to existing methods for haplotype estimation. We show that using a high stringency produces haplotype data with few errors, whereas a low stringency provides haplotype estimates in most situations, but with an increased number of errors.</p> <p>Conclusion</p> <p>By including a stringency criterion in our haplotyping method, the user is able to maintain the error rate at a suitable level for the particular study; one can select anything from haplotyped data with very small proportion of errors and a higher proportion of non-inferred haplotypes, to data with phase estimates for every marker, when haplotype errors are tolerable. Giving this choice makes the method more flexible and useful in a wide range of applications as it is able to fulfil different requirements regarding the tolerance for haplotype errors, or uncertain marker-phases.</p

Accrual and drop out in a primary prevention randomised controlled trial: qualitative study

<p>Abstract</p> <p>Background</p> <p>Recruitment and retention of participants are critical to the success of a randomised controlled trial. Gaining the views of potential trial participants who decline to enter a trial and of trial participants who stop the trial treatment is important and can help to improve study processes. Limited research on these issues has been conducted on healthy individuals recruited for prevention trials in the community.</p> <p>Methods</p> <p>Semi-structured interviews with people who were eligible but had declined to participate in the Aspirin for Asymptomatic Atherosclerosis (AAA) trial (N = 11), and AAA trial participants who had stopped taking the trial medication (N = 11). A focus group with further participants who had stopped taking the trial medication (N = 6). (Total participants N = 28).</p> <p>Results</p> <p>Explanations for declining to participate could be divided into two groups: the first group were characterised by a lack of necessity to participate and a tendency to prioritise other largely mundane problems. The second group's concern was with a high level of perceived risk from participating.</p> <p>Explanations for stopping trial medication fell into four categories: side effects attributed to the trial medication; starting on aspirin or medication contraindicating to aspirin; experiencing an outcome event, and changing one's mind.</p> <p>Conclusions</p> <p>These results indicate that when planning trials (especially in preventive medicine) particular attention should be given to designing appropriate recruitment materials and processes that fully inform potential recruits of the risks and benefits of participation.</p> <p>Trial registration</p> <p>ISRCTN66587262</p