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

Global burden of motor neuron diseases: mind the gaps

2018· letter· en· W2899576798 on OpenAlexaff
Orla Hardiman

Bibliographic record

VenueThe Lancet Neurology · 2018
Typeletter
Languageen
FieldMedicine
TopicAmyotrophic Lateral Sclerosis Research
Canadian institutionsTrinity College
FundersScience Foundation Ireland
KeywordsMotor neuronNeuroscienceBusinessPsychologyComputer scienceMedicine

Abstract

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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

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.125
Threshold uncertainty score0.847

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.039
GPT teacher head0.312
Teacher spread0.273 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations8
Published2018
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