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Enregistrement W2899576798 · doi:10.1016/s1474-4422(18)30398-3

Global burden of motor neuron diseases: mind the gaps

2018· letter· en· W2899576798 sur OpenAlexaff
Orla Hardiman

Notice bibliographique

RevueThe Lancet Neurology · 2018
Typeletter
Langueen
DomaineMedicine
ThématiqueAmyotrophic Lateral Sclerosis Research
Établissements canadiensTrinity College
Organismes subventionnairesScience Foundation Ireland
Mots-clésMotor neuronNeuroscienceBusinessPsychologyComputer scienceMedicine

Résumé

récupéré en direct d'OpenAlex

According to the International Classification of Diseases ninth (ICD-9) and tenth (ICD-10) editions, the category of motor neuron diseases comprises amyotrophic lateral sclerosis, progressive muscular atrophy, primary lateral sclerosis, progressive bulbar palsy, spinal muscular atrophy, and hereditary spastic paraparesis. Spinal muscular atrophy and hereditary spastic paraparesis have a genetic basis, whereas amyotrophic lateral sclerosis, progressive bulbar disease, and primary lateral sclerosis, all of which are adult forms of motor neuron disease, have both familial and sporadic forms. Spinal muscular atrophy is a disease of infancy and childhood, hereditary spastic paraparesis often presents in childhood, and the remaining forms of motor neuron disease occur mostly in people aged older than 50 years. All motor neuron diseases are rare (rare diseases are defined by a prevalence of <1 per 2000 population in Europe),1European CommissionNon-communicable diseases.https://ec.europa.eu/health/non_communicable_diseases/rare_diseases_enDate accessed: October 17, 2018Google Scholar and obtaining sufficient data to generate a global burden for all motor neuron diseases is challenging. By systematic analysis of all available data between 1990 and 2016, from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2016 now reported in The Lancet Neurology, the GBD 2016 Motor Neuron Disease Collaborators have provided the first report of the burden of motor neuron diseases for 195 countries and territories.2GBD 2016 Motor Neuron Disease CollaboratorsGlobal, regional, and national burden of motor neuron disease, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016.Lancet Neurol. 2018; (published online Nov 5.)http://dx.doi.org/10.1016/S1474-4422(18)30404-6Google Scholar Calculating the burden of motor neuron disease for European populations is straightforward. European population-based registers report consistent incidence rates (2–3 per 100 000 person-years) of amyotrophic lateral sclerosis.3Logroscino G Travnor BJ Hardiman O et al.Incidence of amyotrophic lateral sclerosis in Europe.J Neurol Neurosurg Psychiatry. 2010; 81: 385-390Crossref PubMed Scopus (524) Google Scholar Population-based data for individuals of non-European descent are sparse, but incidence rates might be lower in Asia (0·7–0·8 per 100 000 person-years) than European populations.4Chiò A Logroscino G Traynor BJ et al.Global epidemiology of amyotrophic lateral sclerosis: a systematic review of the published literature.Neuroepidemiology. 2013; 41: 118-130Crossref PubMed Scopus (497) Google Scholar The incidence of spinal muscular atrophy varies across populations. This variation is most probably a function of different carrier rates of the disease-causing variants of the SMN gene across different ancestral populations,5Verhaart IEC Robertson A Wilson IJ et al.Prevalence, incidence and carrier frequency of 5q-linked spinal muscular atrophy—a literature review.Orphanet J Rare Dis. 2017; 12: 124Crossref PubMed Scopus (259) Google Scholar whereas the reasons for the geographic variations in incidence of amyotrophic lateral sclerosis are unclear. Amyotrophic lateral sclerosis is a complex genetic disorder, and analysis of data from population-based registers suggests that disease pathogenesis is a six-step process.6Al-Chalabi A Calvo A Chio A et al.Analysis of amyotrophic lateral sclerosis as a multistep process: a population-based modelling study.Lancet Neurol. 2014; 13: 1108-1113Summary Full Text Full Text PDF PubMed Scopus (208) Google Scholar The number of steps is reduced for people carrying a known disease-causing variant, such as a hexanucleotide expansion in C9orf72 or a pathogenic mutation in SOD1.7Chiò A Mazzini L D'Alfonso S et al.The multistep hypothesis of ALS revisited: the role of genetic mutations.Neurology. 2018; 91: e635-e642Crossref PubMed Scopus (94) Google Scholar The frequencies of these mutations vary across ancestral populations, but this variability does not fully account for the non-uniform geographical distribution, as known familial amyotrophic lateral sclerosis accounts for only 10–15% of all cases.8Ryan M Heverin M Doherty MA et al.Determining the incidence of familiality in ALS: a study of temporal trends in Ireland from 1994 to 2016.Neurol Genet. 2018; 4: e239Crossref PubMed Scopus (16) Google Scholar Being of mixed ancestry might be protective in sporadic disease, as a population-based study of mortality in Cuba revealed rates that were lower in the mixed population (0·55 per 100 000 person-years) compared with those primarily of Spanish or African origin (about 0·9 per 100 000 person-years).9Zaldivar T Gutierrez J Lara G Carbonara M Logroscino G Hardiman O Reduced frequency of ALS in an ethnically mixed population: a population-based mortality study.Neurology. 2009; 72: 1640-1645Crossref PubMed Scopus (106) Google Scholar Using all available data, the GBD team have now estimated the years of life lost (YLLs), years of life lived with disability (YLDs), and disability-adjusted life-years (DALYs) associated with motor neuron diseases. The number of people with motor neuron diseases is increasing, but this is mostly attributable to population ageing.1European CommissionNon-communicable diseases.https://ec.europa.eu/health/non_communicable_diseases/rare_diseases_enDate accessed: October 17, 2018Google Scholar The burden of motor neuron diseases is mainly attributable to amyotrophic lateral sclerosis, and is highest in countries with high Socio-demographic Index (SDI; a composite measure of income per capita, education, and fertility), including countries in high-income North America, Australasia, and western Europe; this finding is unsurprising because health services are well developed and provide high standards of clinical care. Age-standardised incidence rates of motor neuron disease are lower in high-income Asia Pacific and because of this, the burden of motor neuron disease is lower in countries in this region than in others with high SDI levels. These findings suggest that causative factors other than sociodemographic development are likely to be responsible for geographic variation in incidence and burden of disease. The geographic variation in disease burden could not be explained by the risk factors available for quantification by the GBD methods, suggesting that additional factors, including ancestral origin and genetic background, might be important in determining risk. By deconstructing the subscales of the amyotrophic lateral sclerosis functional rating scale using a large clinical dataset for amyotrophic lateral sclerosis, the GBD 2016 Motor Neuron Disease Collaborators provide a useful approach for establishing global disability burden and a baseline from which to measure the economic impact of progressive motor decline. However, because this system classifies motor neuron diseases purely on the basis of motor system degeneration, and because we do not yet have a way to capture the extra-motor domains associated with amyotrophic lateral sclerosis reliably, the study could not establish the additional global burden associated with the 50% of patients who develop cognitive and behavioural impairment, and the 13% of patients with amyotrophic lateral sclerosis who have concomitant behavioural variant frontotemporal dementia.10Phukan J Elamin M Bede P et al.The syndrome of cognitive impairment in amyotrophic lateral sclerosis: a population-based study.J Neurol Neurosurg Psychiatry. 2012; 83: 102-118Crossref PubMed Scopus (473) Google Scholar Furthermore, the work relies on incomplete data that were generated between 1996 and 2016—a period that saw growth in our understanding of the wider phenotypes associated with motor neuron diseases, affecting patient ascertainment and disease characterisation.11Hardiman O Al-Chalabi A Brayne C et al.The changing picture of amyotrophic lateral sclerosis: lessons from European registers.J Neurol Neurosurg Psychiatry. 2017; 88: 557-563Crossref PubMed Scopus (78) Google Scholar This increased understanding is particularly true of the cognitive and behavioural aspects of amyotrophic lateral sclerosis, which are now more widely recognised than at the start of the study period. Notwithstanding these limitations, this report of the global burden of motor neuron diseases is an important first step in defining the societal impact of these conditions. The study provides a useful framework within which the global impact of these diseases can be examined, and shows the substantial gaps in our knowledge, particularly relating to understudied populations of non-European or mixed ancestry. I report grants from Science Foundation Ireland and the Irish Health Research Board, and personal fees from Taylor and Francis, Cytokinetics, and Wave Pharmaceuticals. Global, regional, and national burden of motor neuron diseases 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016Motor neuron diseases have low prevalence and incidence, but cause severe disability with a high fatality rate. Incidence of motor neuron diseases has geographical heterogeneity, which is not explained by any risk factors quantified in GBD, suggesting other unmeasured risk factors might have a role. Between 1990 and 2016, the burden of motor neuron diseases has increased substantially. The estimates presented here, as well as future estimates based on data from a greater number of countries, will be important in the planning of services for people with motor neuron diseases worldwide. Full-Text PDF Open Access

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,125
Score d'incertitude au seuil0,847

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,001
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0010,002
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,039
Tête enseignante GPT0,312
Écart entre enseignants0,273 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreCommentaire

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations8
Publié2018
Routes d'admission1
Résumé présentoui

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