Evidence map›Paper›PMID 38009419›Full record

ArticleAnnals of clinical and translational neurology2024

Clinical and imaging predictors of late-onset GM2 gangliosidosis: A scoping review.

Neha P Godbole, Elizabeth Haxton, Olivia E Rowe, Joseph J Locascio, Jeremy D Schmahmann, Florian S Eichler, Eva-Maria Ratai, Christopher D Stephen

Open access · goldAbstract readScoping Review
In one paragraph

Article in Annals of clinical and translational neurology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
1.6field-weighted citation impact, top 16% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed, 9 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 1 institution in 1 country.

Neha P GodboleCenter for Rare Neurological Diseases, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA.
Elizabeth HaxtonCenter for Rare Neurological Diseases, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA.
Olivia E RoweAthinoula A. Martinos Center for Biomedical Imaging, Division of Neuroradiology, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts, USA.ORCID 0000-0002-0577-5927
Joseph J LocascioDepartment of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA.
Jeremy D SchmahmannDepartment of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA.
Florian S EichlerCenter for Rare Neurological Diseases, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA.ORCID 0000-0001-6836-1089
Eva-Maria RataiAthinoula A. Martinos Center for Biomedical Imaging, Division of Neuroradiology, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts, USA.
Christopher D StephenCenter for Rare Neurological Diseases, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA.ORCID 0000-0002-4727-192X
Harvard University · US

Funding

Clinical Trial Readiness In X-Linked Dystonia Parkinsonism: Assessment of Sensor-Based And Blood Biomarkers for Early Detection and Monitoring Disease ProgressionK23NS118045 · NINDS · MASSACHUSETTS GENERAL HOSPITAL · PI STEPHEN, CHRISTOPHER D · 2021 to 2025
$1.1M
NINDS NIH HHS K23 NS118045
6 · The paper itself

Abstract

objectiveLate-onset GM2 gangliosidosis (LOGG) subtypes late-onset Tay-Sachs (LOTS) and Sandhoff disease (LOSD) are ultra-rare neurodegenerative lysosomal storage disorders presenting with weakness, ataxia, and neuropsychiatric symptoms. Previous studies considered LOTS and LOSD clinically indistinguishable; recent studies have challenged this. We performed a scoping review to ascertain whether imaging and clinical features may differentiate these diseases.

methodsWe examined MEDLINE/non-MEDLINE databases up to May 2022. Articles reporting brain imaging findings in genetically/enzymatically confirmed LOGG, symptom onset at age ≥ 10 years (or evaluated at least once ≥18 years) were included, yielding 170 LOGG patients (LOTS = 127, LOSD = 43) across 68 papers. We compared LOTS versus LOSD and performed regression analyses. Results were corrected for multiple comparisons.

resultsAge of onset was lower in LOTS versus LOSD (17.9 ± 8.2 vs. 23.9 ± 14.4 years, p = 0.017), although disease duration was similar (p = 0.34). LOTS more commonly had psychosis/bipolar symptoms (35.0% vs. 9.30%, p = 0.011) but less frequent swallowing problems (4.10% vs. 18.60%, p = 0.041). Cerebellar atrophy was more common in LOTS (89.0%) versus LOSD (60.5%), p < 0.0001, with more severe atrophy in LOTS (p = 0.0005). Brainstem atrophy was documented only in LOTS (14.2%). Independent predictors of LOTS versus LOSD (odds ratio [95% confidence interval]) included the presence of psychosis/bipolar symptoms (4.95 [1.59-19.52], p = 0.011), no swallowing symptoms (0.16 [0.036-0.64], p = 0.011), and cerebellar atrophy (5.81 [2.10-17.08], p = 0.0009). Lower age of onset (0.96 [0.93-1.00], p = 0.075) and tremor (2.50 [0.94-7.43], p = 0.078) were marginally statistically significant but felt relevant to include in the model.

interpretationThese data suggest significant differences in symptomatology, disease course, and imaging findings between LOTS and LOSD.

Indexed as

Gangliosidoses, GM2Neurodegenerative DiseasesPsychotic DisordersAtrophyChildDisease ProgressionHumans

Identifiers

PMID38009419
PMCPMC10791033
OpenAlexW4389038111

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.