ArticleBMC medicine2026
Accelerated brain aging in amyotrophic lateral sclerosis and its prognostic associations: a cohort study.
Article in BMC medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
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.
Who cites it
1 citing paper in PubMed.
- A region-delineated snRNA-seq atlas of mouse spinal cord across lifespan resolves the interaction of normative aging programs with SOD1-G93A ALS.bioRxiv : the preprint server for biology · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
19 authors.
Funding
Abstract
backgroundAmyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease, appearing to be associated with accelerated brain aging. Although increased brain age has been associated with mortality risk in older adults, it remains unclear how the brain aging process is accelerated in ALS and how this acceleration relates to patient prognosis.
methodsOne hundred seventy sporadic ALS patients and 84 age- and sex-matched HCs were recruited and underwent neuropsychological tests and T1-weighted magnetic resonance imaging (MRI) scans. A brain-age prediction model was developed using a 3D-Conformer deep learning framework trained on 4310 healthy T1-weighted MRI data from public datasets. Brain age was predicted in HCs and ALS patients, and then the predicted-age difference (PAD = brain age-chronological age) was calculated. In the ALS cohort, the neuroanatomical association of PAD was examined, and cell-specific gene expression of PAD-related volume loss was analyzed using transcriptomic data from the Allen Human Brain Atlas. Associations between PAD, neuropsychological performance, clinical characteristics, and survival outcomes were further evaluated in ALS patients.
resultsThe brain aging process was accelerated in ALS by an average PAD of 1.77 years, which was predominantly associated with a widespread loss of gray matter volume. Cell-specific gene expression analyses showed microglia, inhibitory neurons, endothelial cells, and pericytes as significant contributors to the accelerated brain aging in ALS, with endothelial cells and pericytes being essential for constructing the blood-brain barrier (BBB) and microglia being involved in neuroinflammation. The acceleration in brain aging was more prominent in older patients and was associated with cognitive impairments. Notably, older brain age at initial diagnosis was an independent risk factor for death (hazard ratio, 1.026; 95% CI [1.008, 1.045]) and was associated with shorter survival and more rapid disease progression.
conclusionsAccelerated brain aging is common in ALS; this could be hypothesized to be associated with the interplay between BBB breakdown and neuroinflammation, leading to gray matter degeneration. Furthermore, older brain age at initial diagnosis is associated with worse clinical outcomes. These findings suggest that therapeutic strategies targeting neuroinflammation and BBB integrity may help attenuate accelerated brain aging and improve prognosis.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.