Causal Inference in the Study of Infectious Disease

Objective: HDL (high-density lipoprotein) cholesterol (HDL-C) and LDL (low-density lipoprotein) cholesterol (LDL-C) are inversely associated with infectious hospitalizations. Whether these represent causal relationships is unknown. Approach and Results: Adults of 40 to 69 years of age were recruited from across the United Kingdom between 2006 and 2010 and followed until March 31, 2016, as part of the UK Biobank. We determined HDL-C, LDL-C, and triglyceride polygenic scores for UK Biobank participants of British white ancestry (n=407 558). We examined the association of lipid levels and polygenic scores with infectious hospitalizations, antibiotic usage, and 28-day sepsis survival using Cox proportional hazards or logistic regression models. Measured levels of HDL-C and LDL-C were inversely associated with risk of infectious hospitalizations, while triglycerides displayed a positive association. A 1-mmol/L increase in genetically determined levels of HDL-C associated with a hazard ratio for infectious disease of 0.84 ([95% CI, 0.75–0.95]; P =0.004). Mendelian randomization using genetic variants associated with HDL-C as an instrumental variable was consistent with a causal relationship between elevated HDL-C and reduced risk of infectious hospitalizations (inverse weighted variance method, P =0.001). Furthermore, of 3222 participants who experienced an index episode of sepsis, there was a significant inverse association between continuous HDL-C polygenic score and 28-day mortality (adjusted hazard ratio, 0.37 [95% CI, 0.14–0.96] per 1 mmol/L increase; P =0.04). LDL-C and triglyceride polygenic scores were not significantly associated with hospitalization for infection, antibiotic use, or sepsis mortality. Conclusions: Our results provide causal inference for an inverse relationship between HDL-C, but not LDL-C or triglycerides, and risk of an infectious hospitalization.

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Limits to Causal Inference with State-Space Reconstruction for Infectious Disease

PLoS ONE ◽

10.1371/journal.pone.0169050 ◽

2016 ◽

Vol 11 (12) ◽

pp. e0169050 ◽

Cited By ~ 23

Author(s):

Sarah Cobey ◽

Edward B. Baskerville

Keyword(s):

Infectious Disease ◽

Causal Inference ◽

State Space ◽

State Space Reconstruction

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Beyond metaphors and semantics: A framework for causal inference in neuroscience

Behavioral and Brain Sciences ◽

10.1017/s0140525x19001389 ◽

2019 ◽

Vol 42 ◽

Author(s):

Roberto A. Gulli

Keyword(s):

Causal Inference ◽

Causal Power

Abstract The long-enduring coding metaphor is deemed problematic because it imbues correlational evidence with causal power. In neuroscience, most research is correlational or conditionally correlational; this research, in aggregate, informs causal inference. Rather than prescribing semantics used in correlational studies, it would be useful for neuroscientists to focus on a constructive syntax to guide principled causal inference.

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Tissue section lifting for electron microscopy

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100081292 ◽

1978 ◽

Vol 36 (2) ◽

pp. 86-87

Author(s):

Adrian F. van Dellen

Keyword(s):

Infectious Disease ◽

Electron Microscopy ◽

Tissue Culture ◽

Organ System ◽

Surrounding Tissue ◽

Paraffin Sections ◽

Surface Areas ◽

Specific Determination ◽

High Degree ◽

Accuracy And Repeatability

The morphologic pathologist may require information on the ultrastructure of a non-specific lesion seen under the light microscope before he can make a specific determination. Such lesions, when caused by infectious disease agents, may be sparsely distributed in any organ system. Tissue culture systems, too, may only have widely dispersed foci suitable for ultrastructural study. In these situations, when only a few, small foci in large tissue areas are useful for electron microscopy, it is advantageous to employ a methodology which rapidly selects a single tissue focus that is expected to yield beneficial ultrastructural data from amongst the surrounding tissue. This is in essence what "LIFTING" accomplishes. We have developed LIFTING to a high degree of accuracy and repeatability utilizing the Microlift (Fig 1), and have successfully applied it to tissue culture monolayers, histologic paraffin sections, and tissue blocks with large surface areas that had been initially fixed for either light or electron microscopy.

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