Measurement and initial characterization of leukocyte telomere length in 474,074 participants in UK Biobank.

Leukocyte telomere length (LTL) is a proposed marker of biological age. Here we report the measurement and initial characterization of LTL in 474,074 participants in UK Biobank. We confirm that older age and male sex associate with shorter LTL, with women on average ~7 years younger in 'biological age' than men. Compared to white Europeans, LTL is markedly longer in African and Chinese ancestries. Older paternal age at birth is associated with longer individual LTL. Higher white cell count is associated with shorter LTL, but proportions of white cell subtypes show weaker associations. Age, ethnicity, sex and white cell count explain ~5.5% of LTL variance. Using paired samples from 1,351 participants taken ~5 years apart, we estimate the within-individual variability in LTL and provide a correction factor for this. This resource provides opportunities to investigate determinants and biomedical consequences of variation in LTL.

Constructing longitudinal disease progression curves using sparse, short-term individual data with an application to Alzheimer's disease.

In epidemiology, cohort studies utilised to monitor and assess disease status and progression often result in short-term and sparse follow-up data. Thus, gaining an understanding of the full-term disease pathogenesis can be difficult, requiring shorter-term data from many individuals to be collated. We investigate and evaluate methods to construct and quantify the underlying long-term longitudinal trajectories for disease markers using short-term follow-up data, specifically applied to Alzheimer's disease. We generate individuals' follow-up data to investigate approaches to this problem adopting a four-step modelling approach that (i) determines individual slopes and anchor points for their short-term trajectory, (ii) fits polynomials to these slopes and anchor points, (iii) integrates the reciprocated polynomials and (iv) inverts the resulting curve providing an estimate of the underlying longitudinal trajectory. To alleviate the potential problem of roots of polynomials falling into the region over which we integrate, we propose the use of non-negative polynomials in Step 2. We demonstrate that our approach can construct underlying sigmoidal trajectories from individuals' sparse, short-term follow-up data. Furthermore, to determine an optimal methodology, we consider variations to our modelling approach including contrasting linear mixed effects regression to linear regression in Step 1 and investigating different orders of polynomials in Step 2. Cubic order polynomials provided more accurate results, and there were negligible differences between regression methodologies. We use bootstrap confidence intervals to quantify the variability in our estimates of the underlying longitudinal trajectory and apply these methods to data from the Alzheimer's Disease Neuroimaging Initiative to demonstrate their practical use. Copyright © 2017 John Wiley & Sons, Ltd.

Point-prevalence study of inappropriate antibiotic use at a tertiary Australian hospital.

A point-prevalence study at a tertiary Australian hospital found 199 of 462 inpatients (43%) to be receiving antibiotic therapy. Forty-seven per cent of antibiotic use was discordant with guidelines or microbiological results and hence considered inappropriate. Risk factors for inappropriate antibiotic prescribing included bone/joint infections, the absence of infection, creatinine level >120 µmol/L, carbapenem or macrolide use and being under the care of the aged care/rehabilitation team. In the setting of finite antimicrobial stewardship resources, identification of local determinants for inappropriate antibiotic use may enable more targeted interventions.