To assess the association of the degree of severity of motor impairment to that of cognitive impairment in a large cohort of patients with amyotrophic lateral sclerosis (ALS).
This is a ...population-based cross-sectional study on patients with ALS incident in Piemonte, Italy, between 2007 and 2015. Cognitive status was classified according to the revised ALS-FTD Consensus Criteria. The King system and the Milano Torino Staging system (MiToS) were used for defining the severity of motor impairment.
Of the 797 patients included in the study, 163 (20.5%) had ALS-frontotemporal dementia (FTD), 38 (4.8%) cognitive and behavioral impairment (ALScbi), 132 (16.6%) cognitive impairment (ALSci), 63 (7.9%) behavioral impairment (ALSbi), 16 (2.0%) nonexecutive impairment, and 385 (48.2%) were cognitively normal. According to King staging, the frequency of cases with ALS-FTD progressively increased from 16.5% in stage 1-44.4% in stage 4; conversely, the frequency of ALSci, ALSbi, and ALScbi increased from King stage 1 to King stage 3 and decreased thereafter. A similar pattern was observed with the MiToS staging. ALS-FTD was more frequent in patients with bulbar involvement at time of cognitive testing. Patients with
expansion (n = 61) showed more severe cognitive impairment with increasing King and MiToS stages.
Our findings suggest that ALS motor and cognitive components may worsen in parallel, and that cognitive impairment becomes more pronounced when bulbar function is involved. Our data support the hypothesis that ALS pathology disseminates in a regional ordered sequence, through a cortico-efferent spreading model.
Amyotrophic lateral sclerosis (ALS) is an adult-onset neurodegenerative disease progressively affecting upper and lower motor neurons in the brain and spinal cord. Mean life expectancy is three to ...five years, with paralysis of muscles, respiratory failure and loss of vital functions being the common causes of death. Clinical manifestations of ALS are heterogeneous due to the mix of anatomic regions involvement and the variability in disease course; consequently, diagnosis and prognosis at the level of individual patient is really challenging. Prediction of ALS progression and stratification of patients into meaningful subgroups have been long-standing interests to clinical practice, research and drug development.
We developed a Dynamic Bayesian Network (DBN) model on more than 4500 ALS patients included in the Pooled Resource Open-Access ALS Clinical Trials Database (PRO-ACT), in order to detect probabilistic relationships among clinical variables and identify risk factors related to survival and loss of vital functions. Furthermore, the DBN was used to simulate the temporal evolution of an ALS cohort predicting survival and the time to impairment of vital functions (communication, swallowing, gait and respiration). A first attempt to stratify patients by risk factors and simulate the progression of ALS subgroups was also implemented.
The DBN model provided the prediction of ALS most probable trajectories over time in terms of important clinical outcomes, including survival and loss of autonomy in functional domains. Furthermore, it allowed the identification of biomarkers related to patients' clinical status as well as vital functions, and unrevealed their probabilistic relationships. For instance, DBN found that bicarbonate and calcium levels influence survival time; moreover, the model evidenced dependencies over time among phosphorus level, movement impairment and creatinine. Finally, our model provided a tool to stratify patients into subgroups of different prognosis studying the effect of specific variables, or combinations of them, on either survival time or time to loss of autonomy in specific functional domains.
The analysis of the risk factors and the simulation allowed by our DBN model might enable better support for ALS prognosis as well as a deeper insight into disease manifestations, in a context of a personalized medicine approach.
ALS etiology and prognostic factors are mostly unknown. Metabolic diseases and especially diabetes mellitus (DM) have been variously related to ALS. However, pieces of evidence have been variegated ...and often conflicting so far. This review aims to give an overview of recent contributions focusing on the relationship between DM and ALS. DM seems to reduce the risk of developing ALS if diagnosed at a younger age; conversely, when diagnosed at an older age, DM seems protective against ALS. Such a relationship was not confirmed in Asian countries where DM increases the risk of ALS independently of the age of onset. Interestingly, DM does not affect ALS prognosis, possibly weakening the potential causal relationship between the two diseases. However, since most studies are observational, it is difficult to state the exact nature of such a relationship and several hypotheses have been made. A recent study using Mendelian randomization suggested that DM is indeed protective against ALS in the European population. However, these analyses are not without limits and further evidence is needed. DM is usually the core of a larger metabolic syndrome. Thus, other metabolic changes such as dyslipidemia, body mass index, and cardiovascular diseases should be collectively considered. Finally, hypermetabolism usually found in ALS patients should be considered too since all these metabolic changes could be compensation (or the cause) of the higher energy expenditure.
Introduction
Few epidemiological studies have assessed the risk of parkinsonisms after prolonged use of neuroleptics. We aimed to examine the long-term risk of degenerative parkinsonisms (DP) ...associated with previous use of neuroleptics.
Methods
All residents in Piedmont, Northern-west Italy, older than 39 years (2,526,319 subjects), were retrospectively followed up from 2013 to 2017. Exposure to neuroleptics was assessed through the regional archive of drug prescriptions. The development of DP was assessed using the regional archives of both drug prescriptions and hospital admissions. We excluded prevalent DP cases at baseline as well as those occurred in the first 18 months (short-term risk). The risk of DP associated with previous use of neuroleptics was examined through Cox regression, using a matched cohort design.
Results
The risk of DP was compared between 63,356 exposed and 316,779 unexposed subjects. A more than threefold higher risk of DP was observed among subjects exposed to antipsychotics, compared to those unexposed (HR = 3.27, 95% CI 3.00–3.57), and was higher for exposure to atypical than typical antipsychotics. The risk decreased after 2 years from therapy cessation but remained significantly elevated (HR = 2.38, 95% CI 1.76–3.21).
Conclusions
These results indicate a high risk of developing DP long time from the start of use and from the cessation for both typical and atypical neuroleptics, suggesting the need of monitoring treated patients even after long-term use and cessation.
Various genetic and environmental risk factors have been implicated in the pathogenesis of amyotrophic lateral sclerosis (ALS). Despite this, the cause of most ALS cases remains obscure. In this ...review, we describe the current evidence implicating genetic and environmental factors in motor neuron degeneration. While the risk exerted by many environmental factors may appear small, their effect could be magnified by the presence of a genetic predisposition. We postulate that gene-environment interactions account for at least a portion of the unknown etiology in ALS. Climate underlies multiple environmental factors, some of which have been implied in ALS etiology, and the impact of global temperature increase on the gene-environment interactions should be carefully monitored. We describe the main concepts underlying such interactions. Although a lack of large cohorts with detailed genetic and environmental information hampers the search for gene-environment interactions, newer algorithms and machine learning approaches offer an opportunity to break this stalemate. Understanding how genetic and environmental factors interact to cause ALS may ultimately pave the way towards precision medicine becoming an integral part of ALS care.
To assess the burden of rare genetic variants and to estimate the contribution of known amyotrophic lateral sclerosis (ALS) genes in an Italian population-based cohort, we performed whole genome ...sequencing in 959 patients with ALS and 677 matched healthy controls.
We performed genome sequencing in a population-based cohort (Piemonte and Valle d'Aosta Registry for ALS PARALS). A panel of 40 ALS genes was analyzed to identify potential disease-causing genetic variants and to evaluate the gene-wide burden of rare variants among our population.
A total of 959 patients with ALS were compared with 677 healthy controls from the same geographical area. Gene-wide association tests demonstrated a strong association with
, whose rare variants are the second most common cause of disease after
expansion. A lower signal was observed for
, proving that its effect on our cohort is driven by a few known causal variants. We detected rare variants in other known ALS genes that did not surpass statistical significance in gene-wise tests, thus highlighting that their contribution to disease risk in our cohort is limited.
We identified potential disease-causing variants in 11.9% of our patients. We identified the genes most frequently involved in our cohort and confirmed the contribution of rare variants in disease risk. Our results provide further insight into the pathologic mechanism of the disease and demonstrate the importance of genome-wide sequencing as a diagnostic tool.
Background and purpose
Social cognition (SC) deficits are included in amyotrophic lateral sclerosis (ALS)–frontotemporal spectrum disorder revised diagnostic criteria. However, SC performance among ...ALS patients is heterogeneous due to the phenotypic variability of the disease and the wide range of neuropsychological tools employed. The aim of the present study was to assess facial emotion recognition and theory of mind in ALS patients compared to controls and to evaluate correlations with the other cognitive domains and degree of motor impairment.
Methods
Eighty‐three patients and 42 controls underwent a cognitive evaluation and SC assessment through the Ekman 60 Faces Test (EK‐60F), the Reading the Mind in the Eyes Test–36 Faces (RMET‐36), and the Story‐Based Empathy Task (SET).
Results
ALS patients showed significantly worse performance compared to controls in EK‐60F global score (p < 0.001), recognition of disgust (p = 0.032), anger (p = 0.038), fear (p < 0.001), and sadness (p < 0.001); RMET‐36 (p < 0.001), and SET global score (p < 0.001). Also, cognitively normal patients (ALS‐CN) showed significantly worse performance compared to controls in EK‐60F global score (p < 0.001), recognition of fear (p = 0.002), sadness (p < 0.001), and SET (p < 0.001). RMET‐36 showed a significant correlation with the Category Fluency Test (p = 0.041). SC tests showed no correlation with motor impairment expressed by Amyotrophic Lateral Sclerosis Functional Rating Scale–Revised.
Conclusions
ALS patients, also when categorized as ALS‐CN, may show impairment in SC performance. The frequent identification of early SC impairment in ALS patients supports the need to routinely assess SC for its impact on end‐of‐life decisions and its potential influence on patients' quality of life.
Social Cognition performances show only minimal correlation with other cognitive domains, included executive functions. The quite frequent identification of an early impairment in Social Cognition supports the need to routinely assess SC for its impact on end‐of‐life‐decision, its potential role as early marker of cognitive impairment and its possible influence on patient's quality of life and burden of care‐givers.
Objectives
To compare the prognostic role of FVC and SVC at diagnosis in amyotrophic lateral sclerosis (ALS) patients.
Methods
We included all patients from the Piemonte and Valle D’Aosta ALS ...register (PARALS) who had been diagnosed with ALS between 1995 and 2015 and underwent spirometry at diagnosis. Survival was considered as time to death/tracheostomy; to assess the prognostic value in typical trial timeframes, survival at 12 and 18 months was calculated too. Cox proportional hazard regression models adjusted by sex, age at diagnosis, diagnostic delay, onset site, and ALSFRS-R total score at the moment of diagnosis were used to assess the prognostic role of FVC and SVC.
Results
A total of 795 ALS patients underwent spirometry at diagnosis during the study period. Four hundred and sixteen (52.3%) performed both FVC and SVC, whereas the others performed FVC only. FVC and SVC values were highly correlated (
r
= 0.92,
p
< 0.001) in the overall population and slightly less correlated in patients with bulbar onset (
r
= 0.86,
p
< 0.001). Both FVC and SVC proved to have a prognostic role with comparable hazard ratios (HRs) (HR 1.83, 95% CI 1.48–2.27 and 1.88, 95% CI 1.51–2.33, respectively). When considering typical trial timeframes, HRs remained similar and were inversely proportional to FVC and SVC values.
Discussion
FVC and SVC at diagnosis can be used interchangeably as independent predictors of survival in both clinical and research settings.
Clinical registers constitute an invaluable resource in the medical data-driven decision making context. Accurate machine learning and data mining approaches on these data can lead to faster ...diagnosis, definition of tailored interventions, and improved outcome prediction. A typical issue when implementing such approaches is the almost unavoidable presence of missing values in the collected data. In this work, we propose an imputation algorithm based on a mutual information-weighted k-nearest neighbours approach, able to handle the simultaneous presence of missing information in different types of variables. We developed and validated the method on a clinical register, constituted by the information collected over subsequent screening visits of a cohort of patients affected by amyotrophic lateral sclerosis.
For each subject with missing data to be imputed, we create a feature vector constituted by the information collected over his/her first three months of visits. This vector is used as sample in a k-nearest neighbours procedure, in order to select, among the other patients, the ones with the most similar temporal evolution of the disease over time. An ad hoc similarity metric was implemented for the sample comparison, capable of handling the different nature of the data, the presence of multiple missing values and include the cross-information among features captured by the mutual information statistic.
We validated the proposed imputation method on an independent test set, comparing its performance with those of three state-of-the-art competitors, resulting in better performance. We further assessed the validity of our algorithm by comparing the performance of a survival classifier built on the data imputed with our method versus the one built on the data imputed with the best-performing competitor.
Imputation of missing data is a crucial -and often mandatory- step when working with real-world datasets. The algorithm proposed in this work could effectively impute an amyotrophic lateral sclerosis clinical dataset, by handling the temporal and the mixed-type nature of the data and by exploiting the cross-information among features. We also showed how the imputation quality can affect a machine learning task.
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