Nanoplastics and Neurodegeneration in ALS.

Plastic production, which exceeds one million tons per year, is of global concern. The constituent low-density polymers enable spread over large distances and micro/nano particles (MNPLs) induce organ toxicity via digestion, inhalation, and skin contact. Particles have been documented in all human tissues including breast milk. MNPLs, especially weathered particles, can breach the blood-brain barrier, inducing neurotoxicity. This has been documented in non-human species, and in human-induced pluripotent stem cell lines. Within the brain, MNPLs initiate an inflammatory response with pro-inflammatory cytokine production, oxidative stress with generation of reactive oxygen species, and mitochondrial dysfunction. Glutamate and GABA neurotransmitter dysfunction also ensues with alteration of excitatory/inhibitory balance in favor of reduced inhibition and resultant neuro-excitation. Inflammation and cortical hyperexcitability are key abnormalities involved in the pathogenic cascade of amyotrophic lateral sclerosis (ALS) and are intricately related to the mislocalization and aggregation of TDP-43, a hallmark of ALS. Water and many foods contain MNPLs and in humans, ingestion is the main form of exposure. Digestion of plastics within the gut can alter their properties, rendering them more toxic, and they cause gut microbiome dysbiosis and a dysfunctional gut-brain axis. This is recognized as a trigger and/or aggravating factor for ALS. ALS is associated with a long (years or decades) preclinical period and neonates and infants are exposed to MNPLs through breast milk, milk substitutes, and toys. This endangers a time of intense neurogenesis and establishment of neuronal circuitry, setting the stage for development of neurodegeneration in later life. MNPL neurotoxicity should be considered as a yet unrecognized risk factor for ALS and related diseases.

Differing patterns of cortical grey matter pathology identified by multifractal analysis in UMN-predominant ALS patients with and without corticospinal tract hyperintensity.

The pathological hallmarks of amyotrophic lateral sclerosis (ALS) are degeneration of the primary motor cortex grey matter (GM) and corticospinal tract (CST) resulting in upper motor neuron (UMN) dysfunction. Conventional brain magnetic resonance imaging (MRI) shows abnormal CST hyperintensity in some UMN-predominant ALS patients (ALS-CST+) but not in others (ALS-CST-). In addition to the CST differences, we aimed to determine whether GM degeneration differs between ALS-CST+ and ALS-CST- patients by cortical thickness (CT), voxel-based morphometry (VBM) and fractal dimension analyses. We hypothesized that MRI multifractal (MF) measures could differentiate between neurologic controls (n = 14) and UMN-predominant ALS patients as well as between patient subgroups (ALS-CST+, n = 21 vs ALS-CST-, n = 27). No significant differences were observed in CT or GM VBM in any brain regions between patients and controls or between ALS subgroups. MF analyses were performed separately on GM of the whole brain, of frontal, parietal, occipital, and temporal lobes as well as of cerebellum. Estimating MF measures D (Q = 0), D (Q = 1), D (Q = 2), Δf, Δα of frontal lobe GM classified neurologic controls, ALS-CST+ and ALS-CST- groups with 98% accuracy and > 95% in F1, recall, precision and specificity scores. Classification accuracy was only 74% when using whole brain MF measures and < 70% for other brain lobes. We demonstrate that MF analysis can distinguish UMN-predominant ALS subgroups based on GM changes, which the more commonly used quantitative approaches of CT and VBM cannot.

Predicting amyotrophic lateral sclerosis (ALS) progression with machine learning.

OBJECTIVE: To predict ALS progression with varying observation and prediction window lengths, using machine learning (ML). METHODS: We used demographic, clinical, and laboratory parameters from 5030 patients in the Pooled Resource Open-Access ALS Clinical Trials (PRO-ACT) database to model ALS disease progression as fast (at least 1.5 points decline in ALS Functional Rating Scale-Revised (ALSFRS-R) per month) or non-fast, using Extreme Gradient Boosting (XGBoost) and Bayesian Long Short Term Memory (BLSTM). XGBoost identified predictors of progression while BLSTM provided a confidence level for each prediction. RESULTS: ML models achieved area under receiver-operating-characteristics curve (AUROC) of 0.570-0.748 and were non-inferior to clinician assessments. Performance was similar with observation lengths of a single visit, 3, 6, or 12 months and on a holdout validation dataset, but was better for longer prediction lengths. 21 important predictors were identified, with the top 3 being days since disease onset, past ALSFRS-R and forced vital capacity. Nonstandard predictors included phosphorus, chloride and albumin. BLSTM demonstrated higher performance for the samples about which it was most confident. Patient screening by models may reduce hypothetical Phase II/III clinical trial sizes by 18.3%. CONCLUSION: Similar accuracies across ML models using different observation lengths suggest that a clinical trial observation period could be shortened to a single visit and clinical trial sizes reduced. Confidence levels provided by BLSTM gave additional information on the trustworthiness of predictions, which could aid decision-making. The identified predictors of ALS progression are potential biomarkers and therapeutic targets for further research.

Describing and characterising variability in ALS disease progression.

BACKGROUND, OBJECTIVES: Decrease in the revised ALS Functional Rating Scale (ALSFRS-R) score is currently the most widely used measure of disease progression. However, it does not sufficiently encompass the heterogeneity of ALS. We describe a measure of variability in ALSFRS-R scores and demonstrate its utility in disease characterization. METHODS: We used 5030 ALS clinical trial patients from the Pooled Resource Open-Access ALS Clinical Trials database to calculate variability in disease progression employing a novel measure and correlated variability with disease span. We characterized the more and less variable populations and designed a machine learning model that used clinical, laboratory and demographic data to predict class of variability. The model was validated with a holdout clinical trial dataset of 84 ALS patients (NCT00818389). RESULTS: Greater variability in disease progression was indicative of longer disease span on the patient-level. The machine learning model was able to predict class of variability with accuracy of 60.1-72.7% across different time periods and yielded a set of predictors based on clinical, laboratory and demographic data. A reduced set of 16 predictors and the holdout dataset yielded similar accuracy. DISCUSSION: This measure of variability is a significant determinant of disease span for fast-progressing patients. The predictors identified may shed light on pathophysiology of variability, with greater variability in fast-progressing patients possibly indicative of greater compensatory reinnervation and longer disease span. Increasing variability alongside decreasing rate of disease progression could be a future aim of trials for faster-progressing patients.

Graph network measures reveal distinct white matter abnormalities in motor and extra-motor brain regions of two UMN-predominant ALS subtypes.

BACKGROUND: Routine clinical magnetic resonance imaging (MRI) shows bilateral corticospinal tract (CST) hyperintensity in some patients with upper motor neuron (UMN)-predominant ALS (ALS-CST+) but not in others (ALS-CST-). Although, similar in their UMN features, the ALS-CST+ patient group is significantly younger in age, has faster disease progression and shorter survival than the ALS-CST- patient group. Reasons for the differences are unclear. METHOD: In order to evaluate more objective MRI measures of these ALS subgroups, we used diffusion tensor images (DTI) obtained using single shot echo planar imaging sequence from 1.5 T Siemens MRI Scanner. We performed an exploratory whole brain white matter (WM) network analysis using graph theory approach on 45 ALS patients (ALS-CST+) (n = 21), and (ALS-CST-) (n = 24) and neurological controls (n = 14). RESULTS: Significant (p < 0.05) differences in nodal degree measure between ALS patients and controls were observed in motor and extra motor regions, supplementary motor area, subcortical WM regions, cerebellum and vermis. Importantly, WM network abnormalities were significantly (p < 0.05) different between ALS-CST+ and ALS-CST- subgroups. Compared to neurologic controls, both ALS subgroups showed hubs in the right superior occipital gyrus and cuneus as well as significantly (p < 0.05) reduced small worldness supportive of WM network damage. CONCLUSIONS: Significant differences between ALS-CST+ and ALS-CST- subgroups of WM network abnormalities, age of onset, symptom duration prior to MRI, and progression rate suggest these patients represent distinct clinical phenotypes and possibly pathophysiologic mechanisms of ALS.

The effects of intervention with intravenous edaravone in Study 19 on hospitalization, tracheostomy, ventilation, and death in patients with amyotrophic lateral sclerosis.

INTRODUCTION/AIMS: Intravenous (IV) edaravone is a US Food and Drug Administration-approved treatment for amyotrophic lateral sclerosis (ALS), shown in clinical trials to slow physical functional decline. In this study we compared the effect of IV edaravone (edaravone-first group) versus placebo followed by IV edaravone (placebo-first group) on survival and additional milestone events. METHODS: This work is a post hoc analysis of Study 19/MCI186-19, which was a randomized, placebo-controlled, phase 3 study investigating IV edaravone versus placebo. Study 19 and its 24-week extension have been described previously (NCT01492686). Edaravone-first versus placebo-first group time to events for specific milestone(s) were analyzed post hoc. Time-to-event composite endpoints were time to death; time to death, tracheostomy, or permanent assisted ventilation (PAV); and time to death, tracheostomy, PAV, or hospitalization. RESULTS: The risk for death, tracheostomy, PAV, or hospitalization was 53% lower among patients in the edaravone-first vs placebo-first groups (hazard ratio = 0.47 [95% confidence interval 0.25 to 0.88], P = .02). The overall effect of IV edaravone on ALS progression could be seen in the significant separation of time-to-event curves for time to death, tracheostomy, PAV, or hospitalization. ALS survival composite endpoint analyses (ALS/SURV) suggested a treatment benefit (least-squares mean difference) for the edaravone-first versus the placebo-first group at week 24 (0.15 ± 0.05 [95% confidence interval 0.06 to 0.25], P < .01) and week 48 (0.11 ± 0.05 [95% confidence interval 0.02 to 0.21], P = .02). DISCUSSION: These analyses illustrate the value of timely and continued IV edaravone treatment, as earlier initiation was associated with a lower risk of death, tracheostomy, PAV, or hospitalization in patients with ALS.

Quantitative Brain MRI Metrics Distinguish Four Different ALS Phenotypes: A Machine Learning Based Study.

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease whose diagnosis depends on the presence of combined lower motor neuron (LMN) and upper motor neuron (UMN) degeneration. LMN degeneration assessment is aided by electromyography, whereas no equivalent exists to assess UMN dysfunction. Magnetic resonance imaging (MRI) is primarily used to exclude conditions that mimic ALS. We have identified four different clinical/radiological phenotypes of ALS patients. We hypothesize that these ALS phenotypes arise from distinct pathologic processes that result in unique MRI signatures. To our knowledge, no machine learning (ML)-based data analyses have been performed to stratify different ALS phenotypes using MRI measures. During routine clinical evaluation, we obtained T1-, T2-, PD-weighted, diffusion tensor (DT) brain MRI of 15 neurological controls and 91 ALS patients (UMN-predominant ALS with corticospinal tract CST) hyperintensity, n = 21; UMN-predominant ALS without CST hyperintensity, n = 26; classic ALS, n = 23; and ALS patients with frontotemporal dementia, n = 21). From these images, we obtained 101 white matter (WM) attributes (including DT measures, graph theory measures from DT and fractal dimension (FD) measures using T1-weighted), 10 grey matter (GM) attributes (including FD based measures from T1-weighted), and 10 non-imaging attributes (2 demographic and 8 clinical measures of ALS). We employed classification and regression tree, Random Forest (RF) and also artificial neural network for the classifications. RF algorithm provided the best accuracy (70-94%) in classifying four different phenotypes of ALS patients. WM metrics played a dominant role in classifying different phenotypes when compared to GM or clinical measures. Although WM measures from both right and left hemispheres need to be considered to identify ALS phenotypes, they appear to be differentially affected by the degenerative process. Longitudinal studies can confirm and extend our findings.

Self-reported factors contributing to delay in ALS diagnosis among primary care providers in a large Ohio-based US healthcare network.

OBJECTIVE: This study's primary objective is to identify self-reported factors that contribute to diagnostic delay in ALS among Primary Care Providers (PCPs). METHODS: A de novo email-based survey was deployed to Ohio-based PCPs in the Cleveland Clinic Health System. RESULTS: Of the 77 PCP participants [including 30 Advance Practice Providers (APPs)] only: (a) 18% of physicians, and 3% of APPs were very confident or confident with recognizing signs and symptoms of ALS, (b) 13% of physicians, and 21% of APP s felt very confident or confident with distinguishing between a neurologic cause of dysfunction from other possible causes, and (c) 23% of physicians, and 11% of APPs felt very confident or confident with distinguishing between upper and lower motor neuron signs. If presented with a weak patient without a specific diagnosis, PCPs most frequently ordered electrodiagnostic testing, brain MRI, cervical or thoracic spine MRI, and serum creatine kinase. PCPs identified top reasons for delayed ALS diagnosis as: (a) patient's delay in seeking medical help, (b) diagnostic uncertainty (c) waiting time for neurology/neuromuscular medicine (NM) consultation. The most desired strategies to shorten diagnostic delay involved: (a) educating PCPs and other non-neurologist "gatekeeper" providers, (b) improving access to specialist neurology care, and (c) developing a reliable diagnostic test for ALS. DISCUSSION: Self-reported factors that increase ALS diagnostic delay among PCPs primarily comprise gaps in clinical knowledge and skills required to detect key symptoms and signs, and suboptimal referral access to a neurology/NM provider. These areas represent important opportunities for targeted improvement efforts.

Hypometabolic and hypermetabolic brain regions in patients with ALS-FTD show distinct patterns of grey and white matter degeneration: A pilot multimodal neuroimaging study.

BACKGROUND: Up to 50% of amyotrophic lateral sclerosis (ALS) patients develop some degree of cognitive dysfunction and a small proportion of these develop frontotemporal dementia (FTD). Non-invasive techniques of magnetic resonance imaging (MRI) and [18F]-fluoro-2-deoxy-d-glucose (18F-FDG) positron emission tomography (PET) have demonstrated structural and metabolic abnormalities, respectively, in the brains of such patients with ALS-FTD. Although initial 18F-FDG PET studies in ALS patients showed only hypometabolism of motor and extramotor brain regions, subsequent studies have demonstrated hypermetabolic changes as well. Such contrasting findings prompted us to hypothesize that hypo- and hypermetabolic brain regions in ALS-FTD patients are associated with divergent degeneration of structural grey matter (GM) and white matter (WM). METHODS: Cerebral glucose metabolic rate (CMRglc), cortical thickness (CT), fractal dimension (FD), and graph theory WM network analyses were performed on clinical MRI and 18F-FDG PET images from 8 ALS-FTD patients and 14 neurologic controls to explore the relationship between GM-WM degeneration and hypo- and hypermetabolic brain regions. RESULTS: CMRglc revealed significant hypometabolism in frontal and precentral gyrus brain regions, with hypermetabolism in temporal, occipital and cerebellar regions. Cortical thinning was noted in both hypo- and hypermetabolic brain areas. Unlike CT, FD did not reveal widespread GM degeneration in hypo- and hypermetabolic brain regions of ALS-FTD patients. Graph theory analysis showed severe WM degeneration in hypometabolic but not hypermetabolic areas, especially in the right hemisphere. CONCLUSION: Our multimodal MRI-PET study provides insights into potentially differential pathophysiological mechanisms between hypo- and hypermetabolic brain regions of ALS-FTD patients.

A new method for estimating under-recruitment of a patient registry: a case study with the Ohio Registry of Amyotrophic Lateral Sclerosis.

We developed a disease registry to collect all incident amyotrophic lateral sclerosis (ALS) cases diagnosed during 2016-2018 in Ohio. Due to incomplete case ascertainment and limitations of the traditional capture-recapture method, we proposed a new method to estimate the number of cases not recruited by the Registry and their spatial distribution. Specifically, we employed three statistical methods to identify reference counties with normal case-population relationships to build a Poisson regression model for estimating case counts in target counties that potentially have unrecruited cases. Then, we conducted spatial smoothing to adjust outliers locally. We validated the estimates with ALS mortality data. We estimated that 119 total cases (95% CI [109, 130]) were not recruited, including 36 females (95% CI [31, 41]) and 83 males (95% CI [74, 99]), and were distributed unevenly across the state. For target counties, including estimated unrecruited cases increased the correlation between the case count and mortality count from r = 0.8494 to 0.9585 for the total, from 0.7573 to 0.8270 for females, and from 0.6862 to 0.9292 for males. The advantage of this method in the spatial perspective makes it an alternative to capture-recapture for estimating cases missed by disease registries.

Graph theory network analysis provides brain MRI evidence of a partial continuum of neurodegeneration in patients with UMN-predominant ALS and ALS-FTD.

OBJECTIVE: Our routine clinical neuroimaging showed hyperintense signal along the corticospinal tract only in some but not all patients with upper motor neuron (UMN)-predominant ALS. ALS patients with CST hyperintensity (ALS-CST+) and those without CST hyperintensity (ALS-CST-) present with nearly identical clinical UMN-predominant symptoms. Some previous studies have suggested that ALS patients with frontotemporal dementia (FTD) are on a continuum with ALS patients without FTD, while others have not. We aimed to determine whether: (a) ALS-CST+, ALS-CST-, and ALS-FTD patients show differential sites of predominant neurodegeneration occurring primarily cortically in the perikaryon or subcortically in the white matter (WM), or (b) UMN-predominant ALS is on a continuum with ALS-FTD. METHODS: Exploratory whole brain grey matter (GM) voxel-based morphometry and WM network analysis using graph theory approach were performed. In this exploratory study, MRI data from 58 ALS patients (ALS-FTD, n = 15; ALS-CST+, n = 19; ALS-CST-, n = 24) and 14 neurological controls were obtained. RESULTS: Significant differences in degree measures (evaluating WM networks) were observed between ALS patients and controls in frontal, motor, extra-motor, subcortical, and cerebellar regions. GM atrophy was observed only in the ALS-FTD subgroup and not in the other ALS subgroups. CONCLUSION: Although WM network disruption by the ALS disease process showed different patterns between ALS-CST+, ALS-CST-, and ALS-FTD subgroups, there were some overlaps, particularly in prefrontal regions and between ALS-CST+ and ALS-FTD patients. Our preliminary findings suggest a partial continuum of, at least, WM degeneration between these subgroups with predominance of cortical pathology ("neuronopathy") in ALS-FTD patients and subcortical WM pathology ("axonopathy") in ALS-CST+ and ALS-CST- patients.

Evidence for generalizability of edaravone efficacy using a novel machine learning risk-based subgroup analysis tool.

Introduction: The edaravone development program for amyotrophic lateral sclerosis (ALS) included trials MCI186-16 (Study 16) and MCI186-19 (Study 19). A cohort enrichment strategy was based on a Study 16 post hoc analysis and applied to Study 19 to elucidate a treatment effect in that study. To determine whether the Study 19 results could be generalized to a broader ALS population, we used a machine learning (ML) model to create a novel risk-based subgroup analysis tool. Methods: A validated ML model was used to rank order all Study 16 participants by predicted time to 50% expected vital capacity. Subjects were stratified into nearest-neighbor risk-based subgroups that were systematically expanded to include the entire Study 16 population. For each subgroup, a statistical analysis generated heat maps that revealed statistically significant effect sizes. Results: A broad region of the Study 16 heat map with significant effect sizes was identified, including up to 70% of the trial population. Incorporating participants identified in the cohort enrichment strategy yielded a broad group comprising 76% of the original participants with a statistically significant treatment effect. This broad group spanned the full range of the functional score progression observed in Study 16. Conclusions: This analysis, applying predictions derived using an ML model to a novel methodology for subgroup identification, ascertained a statistically significant edaravone treatment effect in a cohort of participants with broader disease characteristics than the Study 19 inclusion criteria. This novel methodology may assist clinical interpretation of study results and potentially inform efficient future clinical trial design strategies.

Slowing the loss of physical function in amyotrophic lateral sclerosis with edaravone: Post hoc analysis of ALSFRS-R item scores in pivotal study MCI186-19.

INTRODUCTION: Phase 3 study MCI186-19 demonstrated less loss of physical function with edaravone versus placebo, as measured by the revised Amyotrophic Lateral Sclerosis Functional Rating Scale (ALSFRS-R) total score. A 1-point drop in an individual ALSFRS-R item may be clinically meaningful. We assessed ALSFRS-R item score changes to identify clinical features protected by edaravone treatment. METHODS: Time-to-event analysis was used to assess the cumulative probabilities of reductions in ALSFRS-R item scores and Amyotrophic Lateral Sclerosis Assessment Questionnaire (ALSAQ-40) subdomain scores. RESULTS: Edaravone use was accompanied by: (1) delayed drop of ≥1 point in ALSFRS-R item score for four items: salivation, walking, climbing stairs, orthopnea (unadjusted), or for two items: walking, climbing stairs (after Bonferroni correction for multiple comparisons); (2) delayed score transition from 4 or 3 at baseline to ≤2 for five items: swallowing, eating motion, walking, climbing stairs, orthopnea (unadjusted), or for one item: climbing stairs (after Bonferroni correction for multiple comparisons); and (3) delayed worsening of ALSAQ-40 domain scores representing daily living/independence, eating and drinking (unadjusted). DISCUSSION: These post-hoc analyses identified the ALSFRS-R item scores and ALSAQ-40 domain scores that were associated with preserved gross motor function and health-related quality of life, respectively, after edaravone treatment. Limitations of post-hoc analyses should be considered when interpreting these results. We recommend that clinical trials employing the ALSFRS-R include this type of analysis as a pre-specified secondary outcome measure.

ALS risk factors: Industrial airborne chemical releases.

Most amyotrophic lateral sclerosis (ALS) cases are sporadic (∼90%) and environmental exposures are implicated in their etiology. Large industrial facilities are permitted the airborne release of certain chemicals with hazardous properties and report the amounts to the US Environmental Protection Agency (EPA) as part of its Toxics Release Inventory (TRI) monitoring program. The objective of this project was to identify industrial chemicals released into the air that may be associated with ALS etiology. We geospatially estimated residential exposure to contaminants using a de-identified medical claims database, the SYMPHONY Integrated Dataverse®, with ∼26,000 nationally distributed ALS patients, and non-ALS controls matched for age and gender. We mapped TRI data on industrial releases of 523 airborne contaminants to estimate local residential exposure and used a dynamic categorization algorithm to solve the problem of zero-inflation in the dataset. In an independent validation study, we used residential histories to estimate exposure in each year prior to diagnosis. Air releases with positive associations in both the SYMPHONY analysis and the spatio-temporal validation study included styrene (false discovery rate (FDR) 5.4e-5), chromium (FDR 2.4e-4), nickel (FDR 1.6e-3), and dichloromethane (FDR 4.8e-4). Using a large de-identified healthcare claims dataset, we identified geospatial environmental contaminants associated with ALS. The analytic pipeline used may be applied to other diseases and identify novel targets for exposure mitigation. Our results support the future evaluation of these environmental chemicals as potential etiologic contributors to sporadic ALS risk.

Pesticides applied to crops and amyotrophic lateral sclerosis risk in the U.S.

BACKGROUND: Environmental exposures are implicated in the etiology of amyotrophic lateral sclerosis (ALS). Application of insecticides, herbicides, and fungicides with neurotoxic properties to crops is permitted in the U.S., however reporting of the quantities is government mandated. OBJECTIVE: To identify pesticides that may be associated with ALS etiology for future study. METHODS: We geospatially estimated exposure to crop-applied pesticides as risk factors for ALS in a large de-identified medical claims database, the SYMPHONY Integrated Dataverse®. We extracted residence at diagnosis of ∼26,000 nationally distributed ALS patients, and matched non-ALS controls. We mapped county-level U.S. Geological Survey data on applications of 423 pesticides to estimate local residential exposure. We randomly broke the SYMPHONY dataset into two groups to form independent discovery and validation cohorts, then confirmed top hits using residential history information from a study of NH, VT, and OH. RESULTS: Pesticides with the largest positive statistically significant associations in both the discovery and the validation studies and evidence of neurotoxicity in the literature were the herbicides 2,4-D (OR 1.25 95 % CI 1.17-1.34) and glyphosate (OR 1.29 95 %CI 1.19-1.39), and the insecticides carbaryl (OR 1.32 95 %CI 1.23-1.42) and chlorpyrifos (OR 1.25 95 %CI 1.17-1.33). SIGNIFICANCE: Our geospatial analysis results support potential neurotoxic pesticide exposures as risk factors for sporadic ALS. Focused studies to assess these identified potential relationships are warranted.

Reproductive History and Age of Onset for Women Diagnosed with Amyotrophic Lateral Sclerosis: Data from the National ALS Registry: 2010-2018.

BACKGROUND: Amyotrophic lateral sclerosis (ALS) is a neurological disease of largely unknown etiology with no cure. The National ALS Registry is a voluntary online system that collects demographic and reproductive history (females only) data from patients with ALS. We will examine the association between demographic and reproductive history among female patients aged >18 years and various ages of onset for ALS. METHODS: Data from a cross-sectional study were collected and examined for 1,018 female ALS patients. Patient characteristics examined were demographics including race, BMI, and familial history of ALS. Among patients, information on reproductive history, including age at menopause, ever pregnant, and age at first pregnancy was collected. Unadjusted and adjusted logistic regression models were used to estimate OR and 95% CI in this study. RESULTS: Women were more likely to be diagnosed with ALS before age 60 if they were nonwhite (p = 0.015), had attended college (p = 0.0012), had a normal BMI at age 40 (p < 0.0001), completed menopause before age 50 (p < 0.0001), and had never been pregnant (p = 0.046) in the univariate analysis. Women diagnosed with ALS before age 60 were also more likely to have limb site of onset (p < 0.0001). In the multivariate analysis, those who completed menopause before age 50 were more likely to be diagnosed with ALS before age 60 (OR = 1.8, 95% CI: 1.4-2.3) compared with women who completed menopause at or after age 50, after controlling for race, ever pregnant, age at first pregnancy, family history of ALS, education status, smoking history, and BMI at age 40. For women who were diagnosed with ALS before age 50, the odds of them entering menopause before age 50 climb to 48.7 (95% CI: 11.8, 200.9). The mean age of ALS diagnosis for women who completed menopause before age 50 was 58 years and 64 years for women who entered menopause after age 50 (p < 0.0001). CONCLUSION: Women who reported completing menopause before age 50 were significantly more likely to be diagnosed with ALS before age 60 compared with those who reported entering menopause after age 50. More research is needed to determine the relationship between female reproductive history, especially regarding endogenous estrogen exposure and early-onset ALS.

Corticospinal Tract and Related Grey Matter Morphometric Shape Analysis in ALS Phenotypes: A Fractal Dimension Study.

A pathological hallmark of amyotrophic lateral sclerosis (ALS) is corticospinal tract (CST) degeneration resulting in upper motor neuron (UMN) dysfunction. No quantitative test is available to easily assess UMN pathways. Brain neuroimaging in ALS promises to potentially change this through identifying biomarkers of UMN dysfunction that may accelerate diagnosis and track disease progression. Fractal dimension (FD) has successfully been used to quantify brain grey matter (GM) and white matter (WM) shape complexity in various neurological disorders. Therefore, we investigated CST and whole brain GM and WM morphometric changes using FD analyses in ALS patients with different phenotypes. We hypothesized that FD would detect differences between ALS patients and neurologic controls and even between the ALS subgroups. Neuroimaging was performed in neurologic controls (n = 14), and ALS patients (n = 75). ALS patients were assigned into four groups based on their clinical or radiographic phenotypes. FD values were estimated for brain WM and GM structures. Patients with ALS and frontotemporal dementia (ALS-FTD) showed significantly higher CST FD values and lower primary motor and sensory cortex GM FD values compared to other ALS groups. No other group of ALS patients revealed significant FD value changes when compared to neurologic controls or with other ALS patient groups. These findings support a more severe disease process in ALS-FTD patients compared to other ALS patient groups. FD value measures may be a sensitive index to evaluate GM and WM (including CST) degeneration in ALS patients.

Amyotrophic Lateral Sclerosis Risk, Family Income, and Fish Consumption Estimates of Mercury and Omega-3 PUFAs in the United States.

Most amyotrophic lateral sclerosis (ALS) cases are considered sporadic, without a known genetic basis, and lifestyle factors are suspected to play an etiologic role. We previously observed increased risk of ALS associated with high nail mercury levels as an exposure biomarker and thus hypothesized that mercury exposure via fish consumption patterns increases ALS risk. Lifestyle surveys were obtained from ALS patients (n = 165) and n = 330 age- and sex-matched controls without ALS enrolled in New Hampshire, Vermont, or Ohio, USA. We estimated their annual intake of mercury and omega-3 polyunsaturated fatty acid (PUFA) via self-reported seafood consumption habits, including species and frequency. In our multivariable model, family income showed a significant positive association with ALS risk (p = 0.0003, adjusted for age, sex, family history, education, and race). Neither the estimated annual mercury nor omega-3 PUFA intakes via seafood were associated with ALS risk. ALS incidence is associated with socioeconomic status; however, consistent with a prior international study, this relationship is not linked to mercury intake estimated via fish or seafood consumption patterns.

The Incidence of Amyotrophic Lateral Sclerosis in Ohio 2016-2018: The Ohio Population-Based ALS Registry.

INTRODUCTION: Amyotrophic lateral sclerosis (ALS) is a fatal, neuromuscular disease with no cure. ALS incidence rates have not been assessed specifically in Ohio, yet the state contains both metropolitan and rural areas with a variety of environmental factors that could contribute to disease etiology. We report the incidence of ALS in Ohio residents diagnosed from October 2016 through September 2018. METHODS: We engaged practitioners from 9 Ohio sites to identify newly diagnosed ALS patients and to complete case report forms with demographic and clinical information. ALS was diagnosed according to the Awaji criteria and classified as either definite, probable, or possible. We developed a method to estimate missing cases using a Poisson regression model to impute cases in counties with evidence of undercounting. RESULTS: We identified 333 newly diagnosed ALS patients residing in Ohio during the 2-year index period and found incidence rates varied in the 88 state counties. After incorporating the estimated 27% of missing cases, the corrected crude annual incidence was 1.96/100,000 person-years, and the age- and gender-standardized incidence was 1.71/100,000 person-years (standardized to the 2010 US census). DISCUSSION/CONCLUSION: The estimated Ohio incidence of ALS is overall similar to that reported in other states in the USA. This study reveals a geospatial variation in incidence within the state, and areas with higher rates warrant future investigation.