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Article ; Online: Mortality in a Nationwide Practice-Based Cohort Receiving Paclitaxel-Coated Devices for Lower Limb Peripheral Artery Disease.

Wargny, Matthieu / Leux, Christophe / Chatellier, Gilles / Coudol, Sandrine / Gourraud, Pierre-Antoine / Gouëffic, Yann

Journal of the American College of Cardiology

2024 Volume 83, Issue 13, Page(s) 1207–1221

Abstract: Background: According to a meta-analysis of randomized clinical trials, paclitaxel-coated devices (PCDs) for lower limb endovascular revascularization may be associated with increased risk of late mortality.: Objectives: The purpose of this study was ...

Abstract	Background: According to a meta-analysis of randomized clinical trials, paclitaxel-coated devices (PCDs) for lower limb endovascular revascularization may be associated with increased risk of late mortality. Objectives: The purpose of this study was to determine whether PCDs are associated with all-cause mortality in a real-world setting. Methods: DETECT is a nationwide, exhaustive retrospective cohort study using medico-administrative data from the French National Healthcare System representing >99% of the population. The main selection criterion was the first procedure of interest: endovascular revascularization for lower limb peripheral artery disease involving ≥1 balloon and/or stent performed between October 1, 2011, and December 31, 2019. Patients with or without PCDs were compared for all-cause mortality until December 31, 2021. Results: A total of 259,137 patients were analyzed, with 20,083 (7.7%) treated with ≥1 PCD. After a median follow-up of 4.1 years (Q1-Q3: 2.3-6.4 years), a total of 5,385 deaths/73,923 person-years (PY) (7.3/100 PY) and 109,844 deaths/1,060,513 PY (10.4/100 PY) were observed in the PCD and control groups, respectively. After adjustment for confounding factors, PCD treatment was associated with a lower risk of mortality in multivariable Cox analyses (HR: 0.86; 95% CI: 0.84-0.89; P < 0.001). Similar results were observed using propensity score matching approaches based on either nearest-neighbor or exact matching. Conclusions: In a nationwide analysis based on large-scale real-world data, exposure to PCDs was not associated with a higher risk of mortality in patients undergoing endovascular revascularization for lower limb peripheral artery disease. (The DETECT Project; NCT05254106).
MeSH term(s)	Humans ; Paclitaxel/therapeutic use ; Femoral Artery ; Retrospective Studies ; Peripheral Arterial Disease/surgery ; Peripheral Arterial Disease/diagnosis ; Lower Extremity ; Treatment Outcome ; Popliteal Artery/surgery ; Coated Materials, Biocompatible ; Cardiovascular Agents/therapeutic use ; Angioplasty, Balloon
Chemical Substances	Paclitaxel (P88XT4IS4D) ; Coated Materials, Biocompatible ; Cardiovascular Agents
Language	English
Publishing date	2024-03-25
Publishing country	United States
Document type	Meta-Analysis ; Journal Article
ZDB-ID	605507-2
ISSN	1558-3597 ; 0735-1097
ISSN (online)	1558-3597
ISSN	0735-1097
DOI	10.1016/j.jacc.2024.02.003
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Article: Applying Natural Language Processing to Textual Data From Clinical Data Warehouses: Systematic Review.

Bazoge, Adrien / Morin, Emmanuel / Daille, Béatrice / Gourraud, Pierre-Antoine

JMIR medical informatics

2023 Volume 11, Page(s) e42477

Abstract: Background: In recent years, health data collected during the clinical care process have been often repurposed for secondary use through clinical data warehouses (CDWs), which interconnect disparate data from different sources. A large amount of ... ...

Abstract	Background: In recent years, health data collected during the clinical care process have been often repurposed for secondary use through clinical data warehouses (CDWs), which interconnect disparate data from different sources. A large amount of information of high clinical value is stored in unstructured text format. Natural language processing (NLP), which implements algorithms that can operate on massive unstructured textual data, has the potential to structure the data and make clinical information more accessible. Objective: The aim of this review was to provide an overview of studies applying NLP to textual data from CDWs. It focuses on identifying the (1) NLP tasks applied to data from CDWs and (2) NLP methods used to tackle these tasks. Methods: This review was performed according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. We searched for relevant articles in 3 bibliographic databases: PubMed, Google Scholar, and ACL Anthology. We reviewed the titles and abstracts and included articles according to the following inclusion criteria: (1) focus on NLP applied to textual data from CDWs, (2) articles published between 1995 and 2021, and (3) written in English. Results: We identified 1353 articles, of which 194 (14.34%) met the inclusion criteria. Among all identified NLP tasks in the included papers, information extraction from clinical text (112/194, 57.7%) and the identification of patients (51/194, 26.3%) were the most frequent tasks. To address the various tasks, symbolic methods were the most common NLP methods (124/232, 53.4%), showing that some tasks can be partially achieved with classical NLP techniques, such as regular expressions or pattern matching that exploit specialized lexica, such as drug lists and terminologies. Machine learning (70/232, 30.2%) and deep learning (38/232, 16.4%) have been increasingly used in recent years, including the most recent approaches based on transformers. NLP methods were mostly applied to English language data (153/194, 78.9%). Conclusions: CDWs are central to the secondary use of clinical texts for research purposes. Although the use of NLP on data from CDWs is growing, there remain challenges in this field, especially with regard to languages other than English. Clinical NLP is an effective strategy for accessing, extracting, and transforming data from CDWs. Information retrieved with NLP can assist in clinical research and have an impact on clinical practice.
Language	English
Publishing date	2023-12-15
Publishing country	Canada
Document type	Journal Article ; Review
ZDB-ID	2798261-0
ISSN	2291-9694
ISSN	2291-9694
DOI	10.2196/42477
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Article ; Online: Multiple sclerosis

Douglas S Goodin / Pouya Khankhanian / Pierre-Antoine Gourraud / Nicolas Vince

PLoS ONE, Vol 18, Iss 6, p e

Exploring the limits and implications of genetic and environmental susceptibility.

2023 Volume 0285599

Abstract: Objective To explore and describe the basis and implications of genetic and environmental susceptibility to multiple sclerosis (MS) using the Canadian population-based data. Background Certain parameters of MS-epidemiology are directly observable (e.g., ... ...

Abstract	Objective To explore and describe the basis and implications of genetic and environmental susceptibility to multiple sclerosis (MS) using the Canadian population-based data. Background Certain parameters of MS-epidemiology are directly observable (e.g., the recurrence-risk of MS in siblings and twins, the proportion of women among MS patients, the population-prevalence of MS, and the time-dependent changes in the sex-ratio). By contrast, other parameters can only be inferred from the observed parameters (e.g., the proportion of the population that is "genetically susceptible", the proportion of women among susceptible individuals, the probability that a susceptible individual will experience an environment "sufficient" to cause MS, and if they do, the probability that they will develop the disease). Design/methods The "genetically susceptible" subset (G) of the population (Z) is defined to include everyone with any non-zero life-time chance of developing MS under some environmental conditions. The value for each observed and non-observed epidemiological parameter is assigned a "plausible" range. Using both a Cross-sectional Model and a Longitudinal Model, together with established parameter relationships, we explore, iteratively, trillions of potential parameter combinations and determine those combinations (i.e., solutions) that fall within the acceptable range for both the observed and non-observed parameters. Results Both Models and all analyses intersect and converge to demonstrate that probability of genetic-susceptibitly, P(G), is limited to only a fraction of the population {i.e., P(G) ≤ 0.52)} and an even smaller fraction of women {i.e., P(G│F) < 0.32)}. Consequently, most individuals (particularly women) have no chance whatsoever of developing MS, regardless of their environmental exposure. However, for any susceptible individual to develop MS, requires that they also experience a "sufficient" environment. We use the Canadian data to derive, separately, the exponential response-curves for men and ...
Keywords	Medicine ; R ; Science ; Q
Subject code	333
Language	English
Publishing date	2023-01-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Applying Natural Language Processing to Textual Data From Clinical Data Warehouses

Adrien Bazoge / Emmanuel Morin / Béatrice Daille / Pierre-Antoine Gourraud

JMIR Medical Informatics, Vol 11, p e

Systematic Review

2023 Volume 42477

Abstract: BackgroundIn recent years, health data collected during the clinical care process have been often repurposed for secondary use through clinical data warehouses (CDWs), which interconnect disparate data from different sources. A large amount of ... ...

Abstract	BackgroundIn recent years, health data collected during the clinical care process have been often repurposed for secondary use through clinical data warehouses (CDWs), which interconnect disparate data from different sources. A large amount of information of high clinical value is stored in unstructured text format. Natural language processing (NLP), which implements algorithms that can operate on massive unstructured textual data, has the potential to structure the data and make clinical information more accessible. ObjectiveThe aim of this review was to provide an overview of studies applying NLP to textual data from CDWs. It focuses on identifying the (1) NLP tasks applied to data from CDWs and (2) NLP methods used to tackle these tasks. MethodsThis review was performed according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. We searched for relevant articles in 3 bibliographic databases: PubMed, Google Scholar, and ACL Anthology. We reviewed the titles and abstracts and included articles according to the following inclusion criteria: (1) focus on NLP applied to textual data from CDWs, (2) articles published between 1995 and 2021, and (3) written in English. ResultsWe identified 1353 articles, of which 194 (14.34%) met the inclusion criteria. Among all identified NLP tasks in the included papers, information extraction from clinical text (112/194, 57.7%) and the identification of patients (51/194, 26.3%) were the most frequent tasks. To address the various tasks, symbolic methods were the most common NLP methods (124/232, 53.4%), showing that some tasks can be partially achieved with classical NLP techniques, such as regular expressions or pattern matching that exploit specialized lexica, such as drug lists and terminologies. Machine learning (70/232, 30.2%) and deep learning (38/232, 16.4%) have been increasingly used in recent years, including the most recent approaches based on transformers. NLP methods were mostly applied to English language data (153/194, ...
Keywords	Computer applications to medicine. Medical informatics ; R858-859.7
Subject code	306
Language	English
Publishing date	2023-12-01T00:00:00Z
Publisher	JMIR Publications
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Multiple sclerosis: Exploring the limits and implications of genetic and environmental susceptibility.

Goodin, Douglas S / Khankhanian, Pouya / Gourraud, Pierre-Antoine / Vince, Nicolas

PloS one

2023 Volume 18, Issue 6, Page(s) e0285599

Abstract: Objective: To explore and describe the basis and implications of genetic and environmental susceptibility to multiple sclerosis (MS) using the Canadian population-based data.: Background: Certain parameters of MS-epidemiology are directly observable ( ...

Abstract	Objective: To explore and describe the basis and implications of genetic and environmental susceptibility to multiple sclerosis (MS) using the Canadian population-based data. Background: Certain parameters of MS-epidemiology are directly observable (e.g., the recurrence-risk of MS in siblings and twins, the proportion of women among MS patients, the population-prevalence of MS, and the time-dependent changes in the sex-ratio). By contrast, other parameters can only be inferred from the observed parameters (e.g., the proportion of the population that is "genetically susceptible", the proportion of women among susceptible individuals, the probability that a susceptible individual will experience an environment "sufficient" to cause MS, and if they do, the probability that they will develop the disease). Design/methods: The "genetically susceptible" subset (G) of the population (Z) is defined to include everyone with any non-zero life-time chance of developing MS under some environmental conditions. The value for each observed and non-observed epidemiological parameter is assigned a "plausible" range. Using both a Cross-sectional Model and a Longitudinal Model, together with established parameter relationships, we explore, iteratively, trillions of potential parameter combinations and determine those combinations (i.e., solutions) that fall within the acceptable range for both the observed and non-observed parameters. Results: Both Models and all analyses intersect and converge to demonstrate that probability of genetic-susceptibitly, P(G), is limited to only a fraction of the population {i.e., P(G) ≤ 0.52)} and an even smaller fraction of women {i.e., P(G│F) < 0.32)}. Consequently, most individuals (particularly women) have no chance whatsoever of developing MS, regardless of their environmental exposure. However, for any susceptible individual to develop MS, requires that they also experience a "sufficient" environment. We use the Canadian data to derive, separately, the exponential response-curves for men and women that relate the increasing likelihood of developing MS to an increasing probability that a susceptible individual experiences an environment "sufficient" to cause MS. As the probability of a "sufficient" exposure increases, we define, separately, the limiting probability of developing MS in men (c) and women (d). These Canadian data strongly suggest that: (c < d ≤ 1). If so, this observation establishes both that there must be a "truly" random factor involved in MS pathogenesis and that it is this difference, rather than any difference in genetic or environmental factors, which primarily accounts for the penetrance difference between women and men. Conclusions: The development of MS (in an individual) requires both that they have an appropriate genotype (which is uncommon in the population) and that they have an environmental exposure "sufficient" to cause MS given their genotype. Nevertheless, the two principal findings of this study are that: P(G) ≤ 0.52)} and: (c < d ≤ 1). Threfore, even when the necessary genetic and environmental factors, "sufficient" for MS pathogenesis, co-occur for an individual, they still may or may not develop MS. Consequently, disease pathogenesis, even in this circumstance, seems to involve an important element of chance. Moreover, the conclusion that the macroscopic process of disease development for MS includes a "truly" random element, if replicated (either for MS or for other complex diseases), provides empiric evidence that our universe is non-deterministic.
MeSH term(s)	Male ; Humans ; Female ; Risk Factors ; Multiple Sclerosis/etiology ; Multiple Sclerosis/genetics ; Cross-Sectional Studies ; Canada/epidemiology ; Genetic Predisposition to Disease
Language	English
Publishing date	2023-06-28
Publishing country	United States
Document type	Journal Article
ZDB-ID	2267670-3
ISSN	1932-6203 ; 1932-6203
ISSN (online)	1932-6203
ISSN	1932-6203
DOI	10.1371/journal.pone.0285599
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Management of unruptured intracranial aneurysms: How real-world evidence can help to lift off barriers.

Constant Dit Beaufils, Pacôme / Karakachoff, Matilde / Gourraud, Pierre-Antoine / Bourcier, Romain

Journal of neuroradiology = Journal de neuroradiologie

2023 Volume 50, Issue 2, Page(s) 206–208

MeSH term(s)	Humans ; Intracranial Aneurysm ; Aneurysm, Ruptured ; Risk Factors
Language	English
Publishing date	2023-01-29
Publishing country	France
Document type	Editorial
ZDB-ID	131763-5
ISSN	1773-0406 ; 0150-9861
ISSN (online)	1773-0406
ISSN	0150-9861
DOI	10.1016/j.neurad.2023.01.156
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Je nach Verfügbarkeit (siehe Angabe bei Bestand)
bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
Jg. 1995 - 2021: Lesesall (1.OG)
ab Jg. 2022: Lesesaal (EG)

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Article ; Online: Addressing the Challenges and Barriers to the Integration of Machine Learning into Clinical Practice: An Innovative Method to Hybrid Human-Machine Intelligence.

Ed-Driouch, Chadia / Mars, Franck / Gourraud, Pierre-Antoine / Dumas, Cédric

Sensors (Basel, Switzerland)

2022 Volume 22, Issue 21

Abstract: Machine learning (ML) models have proven their potential in acquiring and analyzing large amounts of data to help solve real-world, complex problems. Their use in healthcare is expected to help physicians make diagnoses, prognoses, treatment decisions, ... ...

Abstract	Machine learning (ML) models have proven their potential in acquiring and analyzing large amounts of data to help solve real-world, complex problems. Their use in healthcare is expected to help physicians make diagnoses, prognoses, treatment decisions, and disease outcome predictions. However, ML solutions are not currently deployed in most healthcare systems. One of the main reasons for this is the provenance, transparency, and clinical utility of the training data. Physicians reject ML solutions if they are not at least based on accurate data and do not clearly include the decision-making process used in clinical practice. In this paper, we present a hybrid human-machine intelligence method to create predictive models driven by clinical practice. We promote the use of quality-approved data and the inclusion of physician reasoning in the ML process. Instead of training the ML algorithms on the given data to create predictive models (conventional method), we propose to pre-categorize the data according to the expert physicians' knowledge and experience. Comparing the results of the conventional method of ML learning versus the hybrid physician-algorithm method showed that the models based on the latter can perform better. Physicians' engagement is the most promising condition for the safe and innovative use of ML in healthcare.
MeSH term(s)	Humans ; Machine Learning ; Artificial Intelligence ; Algorithms ; Physicians ; Delivery of Health Care
Language	English
Publishing date	2022-10-29
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2052857-7
ISSN	1424-8220 ; 1424-8220
ISSN (online)	1424-8220
ISSN	1424-8220
DOI	10.3390/s22218313
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Article ; Online: Addressing the Challenges and Barriers to the Integration of Machine Learning into Clinical Practice

Chadia Ed-Driouch / Franck Mars / Pierre-Antoine Gourraud / Cédric Dumas

Sensors, Vol 22, Iss 8313, p

An Innovative Method to Hybrid Human–Machine Intelligence

2022 Volume 8313

Abstract	Machine learning (ML) models have proven their potential in acquiring and analyzing large amounts of data to help solve real-world, complex problems. Their use in healthcare is expected to help physicians make diagnoses, prognoses, treatment decisions, and disease outcome predictions. However, ML solutions are not currently deployed in most healthcare systems. One of the main reasons for this is the provenance, transparency, and clinical utility of the training data. Physicians reject ML solutions if they are not at least based on accurate data and do not clearly include the decision-making process used in clinical practice. In this paper, we present a hybrid human–machine intelligence method to create predictive models driven by clinical practice. We promote the use of quality-approved data and the inclusion of physician reasoning in the ML process. Instead of training the ML algorithms on the given data to create predictive models (conventional method), we propose to pre-categorize the data according to the expert physicians’ knowledge and experience. Comparing the results of the conventional method of ML learning versus the hybrid physician–algorithm method showed that the models based on the latter can perform better. Physicians’ engagement is the most promising condition for the safe and innovative use of ML in healthcare.
Keywords	human–machine collaboration ; machine learning ; physician–algorithm collaboration ; clinical decision-making ; personalized medicine ; multiple sclerosis ; Chemical technology ; TP1-1185
Subject code	006
Language	English
Publishing date	2022-10-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: The nature of genetic and environmental susceptibility to multiple sclerosis.

Douglas S Goodin / Pouya Khankhanian / Pierre-Antoine Gourraud / Nicolas Vince

PLoS ONE, Vol 16, Iss 3, p e

2021 Volume 0246157

Abstract: Objective To understand the nature of genetic and environmental susceptibility to multiple sclerosis (MS) and, by extension, susceptibility to other complex genetic diseases. Background Certain basic epidemiological parameters of MS (e.g., population- ... ...

Abstract	Objective To understand the nature of genetic and environmental susceptibility to multiple sclerosis (MS) and, by extension, susceptibility to other complex genetic diseases. Background Certain basic epidemiological parameters of MS (e.g., population-prevalence of MS, recurrence-risks for MS in siblings and twins, proportion of women among MS patients, and the time-dependent changes in the sex-ratio) are well-established. In addition, more than 233 genetic-loci have now been identified as being unequivocally MS-associated, including 32 loci within the major histocompatibility complex (MHC), and one locus on the X chromosome. Despite this recent explosion in genetic associations, however, the association of MS with the HLA-DRB115:01~HLA-DQB106:02~a1 (H+) haplotype has been known for decades. Design/methods We define the "genetically-susceptible" subset (G) to include everyone with any non-zero life-time chance of developing MS. Individuals who have no chance of developing MS, regardless of their environmental experiences, belong to the mutually exclusive "non-susceptible" subset (G-). Using these well-established epidemiological parameters, we analyze, mathematically, the implications that these observations have regarding the genetic-susceptibility to MS. In addition, we use the sex-ratio change (observed over a 35-year interval in Canada), to derive the relationship between MS-probability and an increasing likelihood of a sufficient environmental exposure. Results We demonstrate that genetic-susceptibitly is confined to less than 7.3% of populations throughout Europe and North America. Consequently, more than 92.7% of individuals in these populations have no chance whatsoever of developing MS, regardless of their environmental experiences. Even among carriers of the HLA-DRB115:01~HLA-DQB106:02~a1 haplotype, far fewer than 32% can possibly be members the (G) subset. Also, despite the current preponderance of women among MS patients, women are less likely to be in the susceptible (G) subset and have a higher ...
Keywords	Medicine ; R ; Science ; Q
Subject code	333
Language	English
Publishing date	2021-01-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: The nature of genetic and environmental susceptibility to multiple sclerosis.

Goodin, Douglas S / Khankhanian, Pouya / Gourraud, Pierre-Antoine / Vince, Nicolas

PloS one

2021 Volume 16, Issue 3, Page(s) e0246157

Abstract: Objective: To understand the nature of genetic and environmental susceptibility to multiple sclerosis (MS) and, by extension, susceptibility to other complex genetic diseases.: Background: Certain basic epidemiological parameters of MS (e.g., ... ...

Abstract	Objective: To understand the nature of genetic and environmental susceptibility to multiple sclerosis (MS) and, by extension, susceptibility to other complex genetic diseases. Background: Certain basic epidemiological parameters of MS (e.g., population-prevalence of MS, recurrence-risks for MS in siblings and twins, proportion of women among MS patients, and the time-dependent changes in the sex-ratio) are well-established. In addition, more than 233 genetic-loci have now been identified as being unequivocally MS-associated, including 32 loci within the major histocompatibility complex (MHC), and one locus on the X chromosome. Despite this recent explosion in genetic associations, however, the association of MS with the HLA-DRB115:01~HLA-DQB106:02~a1 (H+) haplotype has been known for decades. Design/methods: We define the "genetically-susceptible" subset (G) to include everyone with any non-zero life-time chance of developing MS. Individuals who have no chance of developing MS, regardless of their environmental experiences, belong to the mutually exclusive "non-susceptible" subset (G-). Using these well-established epidemiological parameters, we analyze, mathematically, the implications that these observations have regarding the genetic-susceptibility to MS. In addition, we use the sex-ratio change (observed over a 35-year interval in Canada), to derive the relationship between MS-probability and an increasing likelihood of a sufficient environmental exposure. Results: We demonstrate that genetic-susceptibitly is confined to less than 7.3% of populations throughout Europe and North America. Consequently, more than 92.7% of individuals in these populations have no chance whatsoever of developing MS, regardless of their environmental experiences. Even among carriers of the HLA-DRB115:01~HLA-DQB106:02~a1 haplotype, far fewer than 32% can possibly be members the (G) subset. Also, despite the current preponderance of women among MS patients, women are less likely to be in the susceptible (G) subset and have a higher environmental threshold for developing MS compared to men. Nevertheless, the penetrance of MS in susceptible women is considerably greater than it is in men. Moreover, the response-curves for MS-probability in susceptible individuals increases with an increasing likelihood of a sufficient environmental exposure, especially among women. However, these environmental response-curves plateau at under 50% for women and at a significantly lower level for men. Conclusions: The pathogenesis of MS requires both a genetic predisposition and a suitable environmental exposure. Nevertheless, genetic-susceptibility is rare in the population (< 7.3%) and requires specific combinations of non-additive genetic risk-factors. For example, only a minority of carriers of the HLA-DRB115:01~HLA-DQB106:02~a1 haplotype are even in the (G) subset and, thus, genetic-susceptibility to MS in these carriers must result from the combined effect this haplotype together with the effects of certain other (as yet, unidentified) genetic factors. By itself, this haplotype poses no MS-risk. By contrast, a sufficient environmental exposure (however many events are involved, whenever these events need to act, and whatever these events might be) is common, currently occurring in, at least, 76% of susceptible individuals. In addition, the fact that environmental response-curves plateau well below 50% (especially in men), indicates that disease pathogenesis is partly stochastic. By extension, other diseases, for which monozygotic-twin recurrence-risks greatly exceed the disease-prevalence (e.g., rheumatoid arthritis, diabetes, and celiac disease), must have a similar genetic basis.
MeSH term(s)	Adult ; Alleles ; Environment ; Female ; Genetic Predisposition to Disease ; HLA-DRB1 Chains/genetics ; Haplotypes/genetics ; Humans ; Male ; Multiple Sclerosis/epidemiology ; Multiple Sclerosis/genetics
Chemical Substances	HLA-DRB1 Chains
Language	English
Publishing date	2021-03-22
Publishing country	United States
Document type	Journal Article
ZDB-ID	2267670-3
ISSN	1932-6203 ; 1932-6203
ISSN (online)	1932-6203
ISSN	1932-6203
DOI	10.1371/journal.pone.0246157
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