Statin Use in Relation to Intraocular Pressure, Glaucoma, and Ocular Coherence Tomography Parameters in the UK Biobank

PURPOSE. The purpose of this study was to evaluate the relationship between statin use and glaucoma-related traits. METHODS. In a cross-sectional study, we included 118,153 UK Biobank participants with data on statin use and corneal-compensated IOP. In addition, we included 192,283 participants (8982 cases) with data on glaucoma status. After excluding participants with neurodegenerative diseases, 41,638 participants with macular retinal nerve fiber layer thickness (mRNFL) and 41,547 participants with macular ganglion cell inner plexiform layer thickness (mGCIPL) were available for analysis. We examined associations of statin use with IOP, mRNFL, mGCIPL, and glaucoma status utilizing multivariable-adjusted regression models. We assessed whether a glaucoma polygenic risk score (PRS) modified associations. We performed Mendelian randomization (MR) experiments to investigate associations with various glaucoma-related outcomes. RESULTS. Statin users had higher unadjusted mean IOP ± SD than nonusers, but in a multivariable-adjusted model, IOP did not differ by statin use (difference = 0.05 mm Hg, 95% confidence interval [CI] = −0.02 to 0.13, P = 0.17). Similarly, statin use was not associated with prevalent glaucoma (odds ratio [OR] = 1.05, 95% CI = 0.98 to 1.13). Statin use was weakly associated with thinner mRNFL (difference = −0.15 microns, 95% CI = −0.28 to −0.01, P = 0.03) but not with mGCIPL thickness (difference = −0.12 microns, 95% CI = −0.29 to 0.05, P = 0.17). No association was modified by the glaucoma PRS (Pinteraction ≥ 0.16). MR experiments showed no evidence for a causal association between the cholesterol-altering effect of statins and several glaucoma traits (inverse weighted variance P ≥ 0.14). CONCLUSIONS. We found no evidence of a protective association between statin use and glaucoma or related traits after adjusting for key confounders

A Classification of Faults Covering the Human-Computer Interaction Loop

International audienceThe operator is one of the main sources of vulnerability in command and control systems; for example, 79% of fatal accidents in aviation are attributed to “human error.” Following Avizienis et al.’s classification system for faults human error at operation time can be characterized as the operator’s failure to deliver services while interacting with the command and control system. However, little previous work attempts to separate out the many different origins of faults that set the operator in an error mode. This paper proposes an extension to the Avizienis et al. taxonomy in order to more fully account for the human operator, making explicit the faults, error states, and failures that cause operators to deviate from correct service delivery. Our new taxonomy improves understanding and identification of faults, and provides systematic insight into ways that human service failures could be avoided or repaired. We present multiple concrete examples, from aviation and other domains, of faults affecting operators and fault-tolerant mechanisms, covering the critical aspects of the operator-side of the Human-Computer Interaction Loop