A model of summary data and its applications in statistical databases

Moderate endurance training is known to improve cardiovascular risk factors, and prolongs life expectancy. On the other hand, there has been some discussion whether “too much” exercise might have a contrarious effect by accelerating coronary atherosclerosis. The goal of this review was to evaluate the current literature on the effects of long-term vigorous endurance training on the coronary vasculature. In summary, data point to an increased calcium score, and a higher burden of atherosclerotic plaque in male athletes compared to sedentary controls. However, the plaques found in athletes were more prone to be calcified. The pathogenesis and clinical relevance of this athlete coronary artery disease phenotype remains incompletely understood and represents an area of important future work.

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The effects of long-term vigorous endurance exercise on the coronary arteries

Swiss Sports & Exercise Medicine ◽

10.34045/ssem/2019/35 ◽

2019 ◽

Vol 67 (2) ◽

Keyword(s):

Endurance Training ◽

Coronary Atherosclerosis ◽

Calcium Score ◽

The Other ◽

Disease Phenotype ◽

Male Athletes ◽

Artery Disease ◽

Future Work ◽

Summary Data

Moderate endurance training is known to improve cardiovascular risk factors, and prolongs life expectancy. On the other hand, there has been some discussion whether “too much” exercise might have a contrarious effect by accelerating coronary atherosclerosis. The goal of this review was to evaluate the current literature on the effects of long-term vigorous endurance training on the coronary vasculature. In summary, data point to an increased calcium score, and a higher burden of atherosclerotic plaque in male athletes compared to sedentary controls. However, the plaques found in athletes were more prone to be calcified. The pathogenesis and clinical relevance of this athlete coronary artery disease phenotype remains incompletely understood and represents an area of important future work.

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Faculty Opinions recommendation of XGR software for enhanced interpretation of genomic summary data, illustrated by application to immunological traits.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.727108713.793532803 ◽

2017 ◽

Author(s):

Alison Motsinger-Reif

Keyword(s):

Summary Data

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Mendelian randomisation for mediation analysis: current methods and challenges for implementation

European Journal of Epidemiology ◽

10.1007/s10654-021-00757-1 ◽

2021 ◽

Author(s):

Alice R. Carter ◽

Eleanor Sanderson ◽

Gemma Hammerton ◽

Rebecca C. Richmond ◽

George Davey Smith ◽

...

Keyword(s):

Measurement Error ◽

Causal Inference ◽

Mediation Analysis ◽

Instrumental Variable ◽

Real Data ◽

Mendelian Randomisation ◽

Differential Measurement ◽

Individual Level ◽

Differential Measurement Error ◽

Summary Data

AbstractMediation analysis seeks to explain the pathway(s) through which an exposure affects an outcome. Traditional, non-instrumental variable methods for mediation analysis experience a number of methodological difficulties, including bias due to confounding between an exposure, mediator and outcome and measurement error. Mendelian randomisation (MR) can be used to improve causal inference for mediation analysis. We describe two approaches that can be used for estimating mediation analysis with MR: multivariable MR (MVMR) and two-step MR. We outline the approaches and provide code to demonstrate how they can be used in mediation analysis. We review issues that can affect analyses, including confounding, measurement error, weak instrument bias, interactions between exposures and mediators and analysis of multiple mediators. Description of the methods is supplemented by simulated and real data examples. Although MR relies on large sample sizes and strong assumptions, such as having strong instruments and no horizontally pleiotropic pathways, our simulations demonstrate that these methods are unaffected by confounders of the exposure or mediator and the outcome and non-differential measurement error of the exposure or mediator. Both MVMR and two-step MR can be implemented in both individual-level MR and summary data MR. MR mediation methods require different assumptions to be made, compared with non-instrumental variable mediation methods. Where these assumptions are more plausible, MR can be used to improve causal inference in mediation analysis.

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