78 research outputs found
New statistical method identifes cytokines that distinguish stool microbiomes
Regressing an outcome or dependent variable onto a set of input or independent variables allows the analyst to measure associations between the two so that changes in the outcome can be described by and predicted by changes in the inputs. While there are many ways of doing this in classical statistics, where the dependent variable has certain properties (e.g., a scalar, survival time, count), little progress on regression where the dependent variable are microbiome taxa counts has been made that do not impose extremely strict conditions on the data. In this paper, we propose and apply a new regression model combining the Dirichlet-multinomial distribution with recursive partitioning providing a fully non-parametric regression model. This model, called DM-RPart, is applied to cytokine data and microbiome taxa count data and is applicable to any microbiome taxa count/metadata, is automatically fit, and intuitively interpretable. This is a model which can be applied to any microbiome or other compositional data and software (R package HMP) available through the R CRAN website
Risk factors for incident carpal tunnel syndrome: results of a prospective cohort study of newly-hired workers
Carpal tunnel syndrome is one of the most costly upper extremity disorders in the working population. Past literature has shown an association between personal and work factors to a case definition of carpal tunnel syndrome but little is known about the combined effects of these factors with the development of this disorder. Few studies have examined these associations in longitudinal studies. The purpose of this paper is to identify risk factors for incident carpal tunnel syndrome in a longitudinal study of workers across a wide range of occupations
Topical decolonization does not eradicate the skin microbiota of community-dwelling or hospitalized adults
Topical antimicrobials are often employed for decolonization and infection prevention and may alter the endogenous microbiota of the skin. The objective of this study was to compare the microbial communities and levels of richness and diversity in community-dwelling subjects and intensive care unit (ICU) patients before and after the use of topical decolonization protocols. We enrolled 15 adults at risk for Staphylococcus aureus infection. Community subjects (n = 8) underwent a 5-day decolonization protocol (twice daily intranasal mupirocin and daily dilute bleach-water baths), and ICU patients (n = 7) received daily chlorhexidine baths. Swab samples were collected from 5 anatomic sites immediately before and again after decolonization. A variety of culture media and incubation environments were used to recover bacteria and fungi; isolates were identified using matrix-assisted laser desorption ionization–time of flight mass spectrometry. Overall, 174 unique organisms were recovered. Unique communities of organisms were recovered from the community-dwelling and hospitalized cohorts. In the community-dwelling cohort, microbial richness and diversity did not differ significantly between collections across time points, although the number of body sites colonized with S. aureus decreased significantly over time (P = 0.004). Within the hospitalized cohort, richness and diversity decreased over time compared to those for the enrollment sampling (from enrollment to final sampling, P = 0.01 for both richness and diversity). Topical antimicrobials reduced the burden of S. aureus while preserving other components of the skin and nasal microbiota
Association of early versus delayed normalisation of left ventricular ejection fraction with mortality in ischemic cardiomyopathy
OBJECTIVE: In patients with non-ischaemic cardiomyopathy and reduced left ventricular ejection fraction (LVEF), normalisation of LVEF is associated with improved outcomes. However, data on patients with ischaemic cardiomyopathy and recovered LVEF are lacking. The goal of this study was to assess the prognostic significance of normalisation of the LVEF in patients with ischaemic cardiomyopathy.
METHODS/RESULTS: We performed a non-prespecified post hoc analysis of the Surgical Treatment for Ischaemic Heart Failure (STICH) trial to determine the association between normalisation of LVEF (\u3e50%) and mortality during follow-up. Of the 1212 patients with LVEF \u3c35% enroled in the STICH trial, 932 underwent assessment of LVEF at 4 months and/or 2 years after enrolment. Among them, 18 patients experienced normalisation in LVEF at 4-month follow-up and 35 patients experienced recovery in LVEF at 2 years. Recovery of LVEF at 4 months and recovery of LVEF at 2 years were not correlated. Recovery of LVEF at 4 months was not associated with reduced all-cause mortality in unadjusted analysis (log-rank test p=0.54) or in Cox proportional hazards analysis (HR: 0.93; 95% CI: 0.48 to 1.80; p=0.82). Ejection fraction recovery at 2 years was associated with a reduction in all-cause mortality, both in unadjusted analysis (log-rank test p=0.004) and in the Cox proportional hazard model (HR: 0.41; 95% CI: 0.21 to 0.80; p=0.009).
CONCLUSIONS: In patients with ischaemic cardiomyopathy, delayed normalisation of LVEF is associated with reduced mortality, whereas early recovery of LVEF is not. Further studies are needed to confirm these findings
New statistical method identifies cytokines that distinguish stool microbiomes.
Regressing an outcome or dependent variable onto a set of input or independent variables allows the analyst to measure associations between the two so that changes in the outcome can be described by and predicted by changes in the inputs. While there are many ways of doing this in classical statistics, where the dependent variable has certain properties (e.g., a scalar, survival time, count), little progress on regression where the dependent variable are microbiome taxa counts has been made that do not impose extremely strict conditions on the data. In this paper, we propose and apply a new regression model combining the Dirichlet-multinomial distribution with recursive partitioning providing a fully non-parametric regression model. This model, called DM-RPart, is applied to cytokine data and microbiome taxa count data and is applicable to any microbiome taxa count/metadata, is automatically fit, and intuitively interpretable. This is a model which can be applied to any microbiome or other compositional data and software (R package HMP) available through the R CRAN website
Sex-related differences in premature cardiovascular disease in familial hypercholesterolemia
BACKGROUND: Familial hypercholesterolemia (FH) is associated with an increased prevalence of premature atherosclerotic cardiovascular disease (ASCVD), however, little is known about sex-specific differences in premature ASCVD and its risk factors.
OBJECTIVE: The present study seeks to assess the burden and risk factors for premature ASCVD among men and women with FH.
METHODS: In this study we retrospectively examined sex-specific differences in ASCVD prevalence, risk factor burdens, and lipid treatment outcomes in 782 individuals with clinically or genetically confirmed FH treated in 5 U.S. lipid and genetics clinics. A generalized linear model using Binomial distribution with random study site effect and sex-stratified analysis was used to determine the strongest predictors of premature ASCVD, and lipid treatment outcomes. Covariates included age, sex, diabetes mellitus (DM), hypertension, and current smoking.
RESULTS: Among the cohort, 98/280 men (35%) and 89/502 women (18%) had premature ASCVD (defined as \u3c55 years in men and \u3c65 years in women). Women with premature ASCVD had higher mean treated total cholesterol (216 vs. 179 mg/dl, p=\u3c0.001) and LDL-C (135 vs. 109 mg/dl, p= 0.005).
CONCLUSION: These data confirm that high percentages of women and men with FH develop premature ASCVD, and suggest that FH may narrow the observed sex difference in premature ASCVD onset. These data support more aggressive prevention and treatment strategies in FH, including in women, to reduce non-lipid risk factors and residual hypercholesterolemia
Measuring the impact of apnea and obesity on circadian activity patterns using functional linear modeling of actigraphy data
<p>Abstract</p> <p>Background</p> <p>Actigraphy provides a way to objectively measure activity in human subjects. This paper describes a novel family of statistical methods that can be used to analyze this data in a more comprehensive way.</p> <p>Methods</p> <p>A statistical method for testing differences in activity patterns measured by actigraphy across subgroups using functional data analysis is described. For illustration this method is used to statistically assess the impact of apnea-hypopnea index (apnea) and body mass index (BMI) on circadian activity patterns measured using actigraphy in 395 participants from 18 to 80 years old, referred to the Washington University Sleep Medicine Center for general sleep medicine care. Mathematical descriptions of the methods and results from their application to real data are presented.</p> <p>Results</p> <p>Activity patterns were recorded by an Actical device (Philips Respironics Inc.) every minute for at least seven days. Functional linear modeling was used to detect the association between circadian activity patterns and apnea and BMI. Results indicate that participants in high apnea group have statistically lower activity during the day, and that BMI in our study population does not significantly impact circadian patterns.</p> <p>Conclusions</p> <p>Compared with analysis using summary measures (e.g., average activity over 24 hours, total sleep time), Functional Data Analysis (FDA) is a novel statistical framework that more efficiently analyzes information from actigraphy data. FDA has the potential to reposition the focus of actigraphy data from general sleep assessment to rigorous analyses of circadian activity rhythms.</p
Hypothesis Testing and Power Calculations for Taxonomic-Based Human Microbiome Data
This paper presents new biostatistical methods for the analysis of microbiome data based on a fully parametric approach using all the data. The Dirichlet-multinomial distribution allows the analyst to calculate power and sample sizes for experimental design, perform tests of hypotheses (e.g., compare microbiomes across groups), and to estimate parameters describing microbiome properties. The use of a fully parametric model for these data has the benefit over alternative non-parametric approaches such as bootstrapping and permutation testing, in that this model is able to retain more information contained in the data. This paper details the statistical approaches for several tests of hypothesis and power/sample size calculations, and applies them for illustration to taxonomic abundance distribution and rank abundance distribution data using HMP Jumpstart data on 24 subjects for saliva, subgingival, and supragingival samples. Software for running these analyses is available
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