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Deutsches Arzteblatt International Oct 2020
Topics: Causality; Cervix Uteri; Female; Humans
PubMed: 33559600
DOI: 10.3238/arztebl.2020.0735c -
Nature Reviews. Immunology Aug 2021A new study in provides a single-cell map of the epigenetic and transcriptomic landscape in response to influenza vaccination, revealing persistent epigenomic...
A new study in provides a single-cell map of the epigenetic and transcriptomic landscape in response to influenza vaccination, revealing persistent epigenomic remodelling in myeloid cells and the antiviral effects of adjuvant.
Topics: Causality; Epigenome; Humans
PubMed: 34239105
DOI: 10.1038/s41577-021-00585-0 -
Journal of Parkinson's Disease 2022Parkinson's disease (PD) is increasingly recognised as a systemic disorder in which inflammation might play a causative role rather than being a consequence or an... (Review)
Review
Parkinson's disease (PD) is increasingly recognised as a systemic disorder in which inflammation might play a causative role rather than being a consequence or an epiphenomenon of the neurodegenerative process. Although growing genetic evidence links the central and peripheral immune system with both monogenic and sporadic PD, our understanding on how the immune system contributes to PD pathogenesis remains a daunting challenge. In this review, we discuss recent literature aimed at exploring the role of known genes and susceptibility loci to PD pathogenesis through immune system related mechanisms. Furthermore, we outline shared genetic etiologies and interrelations between PD and autoimmune diseases and underlining challenges and limitations faced in the translation of relevant allelic and regulatory risk loci to immune-pathological mechanisms. Lastly, with the field of immunogenetics expanding rapidly, we place these insights into a future context highlighting the prospect of immune modulation as a promising disease-modifying strategy.
Topics: Causality; Humans; Immune System; Immunogenetics; Inflammation; Parkinson Disease
PubMed: 35367971
DOI: 10.3233/JPD-223176 -
Comptes Rendus Biologies 2019This article is the result of a symposium organized at the French Academy of Sciences on May 31, 2016, entitled: Do we need to know the causes to understand and... (Review)
Review
This article is the result of a symposium organized at the French Academy of Sciences on May 31, 2016, entitled: Do we need to know the causes to understand and intervene? Questions on causality in the biological and medical sciences, and published in French in a book entitled: Causality in the biological and medical sciences (EDP Sciences, 2017).
Topics: Academies and Institutes; Causality; Humans
PubMed: 30981720
DOI: 10.1016/j.crvi.2019.03.001 -
Frontiers in Endocrinology 2023
Topics: Humans; Causality; Neoplasms; Obesity
PubMed: 37645420
DOI: 10.3389/fendo.2023.1258994 -
Ginekologia Polska 2019Cerebral palsy is a disease that puts a great mental burden on caregivers and generates very high social costs. Children with CP require many years of rehabilitation and... (Review)
Review
Cerebral palsy is a disease that puts a great mental burden on caregivers and generates very high social costs. Children with CP require many years of rehabilitation and medical care. The etiology of the disease is undoubtedly multifactorial, and the pathogenesis is associated with focal damage to the central nervous system. One can find descriptions of well-documented interventions in the literature that reduce the risk of CP in certain groups of pregnant and neonatal patients, and interventions that have a potentially protective effect. In this review, we have analyzed the available literature in terms of prenatal and postnatal interventions that may have an impact on reducing the incidence of this condition in children.
Topics: Causality; Cerebral Palsy; Female; Humans; Infant, Newborn; Neonatology; Obstetrics; Pregnancy; Preventive Medicine; Protective Factors
PubMed: 31909467
DOI: 10.5603/GP.2019.0124 -
The Lancet. Public Health Apr 2024Life course epidemiology aims to study the effect of exposures on health outcomes across the life course from a social, behavioural, and biological perspective. In this... (Review)
Review
Life course epidemiology aims to study the effect of exposures on health outcomes across the life course from a social, behavioural, and biological perspective. In this Review, we describe how life course epidemiology changes the way the causes of chronic diseases are understood, with the example of hypertension, breast cancer, and dementia, and how it guides prevention strategies. Life course epidemiology uses complex methods for the analysis of longitudinal, ideally population-based, observational data and takes advantage of new approaches for causal inference. It informs primordial prevention, the prevention of exposure to risk factors, from an eco-social and life course perspective in which health and disease are conceived as the results of complex interactions between biological endowment, health behaviours, social networks, family influences, and socioeconomic conditions across the life course. More broadly, life course epidemiology guides population-based and high-risk prevention strategies for chronic diseases from the prenatal period to old age, contributing to evidence-based and data-informed public health actions. In this Review, we assess the contribution of life course epidemiology to public health and reflect on current and future challenges for this field and its integration into policy making.
Topics: Pregnancy; Female; Humans; Public Health; Life Change Events; Risk Factors; Causality; Chronic Disease
PubMed: 38553145
DOI: 10.1016/S2468-2667(24)00018-5 -
Journal of Translational Medicine Dec 2022Ophthalmic epidemiology is concerned with the prevalence, distribution and other factors relating to human eye disease. While observational studies cannot avoid... (Review)
Review
Ophthalmic epidemiology is concerned with the prevalence, distribution and other factors relating to human eye disease. While observational studies cannot avoid confounding factors from interventions, human eye composition and structure are unique, thus, eye disease pathogenesis, which greatly impairs quality of life and visual health, remains to be fully explored. Notwithstanding, inheritance has had a vital role in ophthalmic disease. Mendelian randomization (MR) is an emerging method that uses genetic variations as instrumental variables (IVs) to avoid confounders and reverse causality issues; it reveals causal relationships between exposure and a range of eyes disorders. Thus far, many MR studies have identified potentially causal associations between lifestyles or biological exposures and eye diseases, thus providing opportunities for further mechanistic research, and interventional development. However, MR results/data must be interpreted based on comprehensive evidence, whereas MR applications in ophthalmic epidemiology have some limitations worth exploring. Here, we review key principles, assumptions and MR methods, summarise contemporary evidence from MR studies on eye disease and provide new ideas uncovering aetiology in ophthalmology.
Topics: Humans; Mendelian Randomization Analysis; Quality of Life; Causality; Eye Diseases; Human Genetics; Genetic Variation
PubMed: 36572895
DOI: 10.1186/s12967-022-03822-9 -
ELife Aug 2022Complex systems are challenging to understand, especially when they defy manipulative experiments for practical or ethical reasons. Several fields have developed... (Review)
Review
Complex systems are challenging to understand, especially when they defy manipulative experiments for practical or ethical reasons. Several fields have developed parallel approaches to infer causal relations from observational time series. Yet, these methods are easy to misunderstand and often controversial. Here, we provide an accessible and critical review of three statistical causal discovery approaches (pairwise correlation, Granger causality, and state space reconstruction), using examples inspired by ecological processes. For each approach, we ask what it tests for, what causal statement it might imply, and when it could lead us astray. We devise new ways of visualizing key concepts, describe some novel pathologies of existing methods, and point out how so-called 'model-free' causality tests are not assumption-free. We hope that our synthesis will facilitate thoughtful application of methods, promote communication across different fields, and encourage explicit statements of assumptions. A video walkthrough is available (Video 1 or https://youtu.be/AIV0ttQrjK8).
Topics: Causality; Time Factors
PubMed: 35983746
DOI: 10.7554/eLife.72518 -
Epidemiology (Cambridge, Mass.) Sep 2019A common reason given for assessing interaction is to evaluate "whether the effect is larger in one group versus another". It has long been known that the answer to this... (Review)
Review
A common reason given for assessing interaction is to evaluate "whether the effect is larger in one group versus another". It has long been known that the answer to this question is scale dependent: the "effect" may be larger for one subgroup on the difference scale, but smaller on the ratio scale. In this article, we show that if the relative magnitude of effects across subgroups is of interest then there exists an "interaction continuum" that characterizes the nature of these relations. When both main effects are positive then the placement on the continuum depends on the relative magnitude of the probability of the outcome in the doubly exposed group. For high probabilities of the outcome in the doubly exposed group, the interaction may be positive-multiplicative positive-additive, the strongest form of positive interaction on the "interaction continuum". As the probability of the outcome in the doubly exposed group goes down, the form of interaction descends through ranks, of what we will refer to as the following: positive-multiplicative positive-additive, no-multiplicative positive-additive, negative-multiplicative positive-additive, negative-multiplicative zero-additive, negative-multiplicative negative-additive, single pure interaction, single qualitative interaction, single-qualitative single-pure interaction, double qualitative interaction, perfect antagonism, inverted interaction. One can thus place a particular set of outcome probabilities into one of these eleven states on the interaction continuum. Analogous results are also given when both exposures are protective, or when one is protective and one causative. The "interaction continuum" can allow for inquiries as to relative effects sizes, while also acknowledging the scale dependence of the notion of interaction itself.
Topics: Causality; Effect Modifier, Epidemiologic; Environmental Exposure; Humans; Probability; Protective Factors
PubMed: 31205287
DOI: 10.1097/EDE.0000000000001054