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Record W7117325764 · doi:10.1002/alz70856_103532

TMEM106B modulates disease severity in genetic frontotemporal dementia phenoconverters

2025· article· en· W7117325764 on OpenAlexaff
Maurice Pasternak, Saira S. Mirza, Andrew D. Paterson, Carmela M. Tartaglia, Sara Mitchell, Sandra E. Black, Morris Freedman, David F. Tang‐Wai, Ekaterina Rogaeva, David M. Cash, Martina Bocchetta, John van Swieten, Robert Laforce, Fabrizio Tagliavini, Barbara Borroni, Daniela Galimberti, Caroline Graff, Elizabeth Finger, Sandro Sorbi, Alexandre de Mendonça, Christopher Butler, Alexander Gerhard, Raquel Sánchez‐Valle, Fermin Moreno, Matthis Synofzik, Rik Vandenberghe, Simon Ducharme, Johannes Levin, Markus Otto, Isabel Santana, Jonathan D. Rohrer, Mario Masellis, GENFI

Bibliographic record

VenueAlzheimer s & Dementia · 2025
Typearticle
Languageen
FieldMedicine
TopicAmyotrophic Lateral Sclerosis Research
Canadian institutionsMcGill UniversityDouglas Mental Health University InstituteUniversity of TorontoWestern UniversityUniversité LavalHealth Sciences CentreUniversity Health NetworkOccupational Cancer Research CentreBaycrest HospitalHospital for Sick ChildrenOntario Brain InstituteMontreal Neurological Institute and HospitalToronto Western HospitalSunnybrook Health Science Centre
Fundersnot available
KeywordsFrontotemporal dementiaDiseaseGenetic predispositionDementiaPathogenesisGene

Abstract

fetched live from OpenAlex

Abstract Background A common variant within TMEM106B is associated with risk for Frontotemporal Lobar Degeneration‐Tar DNA binding Protein‐43 (FTLD‐TDP). A recent study has shown that the minor allele G of TMEM106B‐rs1990622 confers protection against FTLD‐TDP in symptomatic mutation carriers through reductions in NfL serum levels, brain atrophy, and cognitive decline. It is unknown whether this protective effect is present in phenoconverters of the disease. Method We included 518 participants from the GENetic Frontotemporal dementia Initiative (GENFI), which recruits genetic FTD cases and their family members, both carriers and non‐carriers of FTD mutations. Of these, 21 were phenoconverters, 209 were non‐carrier controls, 70 were presymptomatic and 45 symptomatic C9orf72 carriers, 92 presymptomatic and 29 symptomatic GRN carriers, and 39 presymptomatic and 13 symptomatic MAPT carriers. Effects of interaction between TMEM106B‐rs1990622 and phenoconverter status were examined using mixed effects models, with a random effects structure featuring subjects nested within families and fixed effects for age at baseline and sex. Serum neurofilament light chain (NfL) was measured using the Simoa platform. Cognitive assessment included the Mini‐Mental State Examination (MMSE), tests of attention, processing speed, executive function, and language, as well as the Cambridge Behavioural Inventory (CBI), with mixed effects also including years of education as a covariate. Brain volumetry was assessed using T1‐weighted MRI and these mixed effect models also included additional covariates of total intracranial volume and scanner site. Result In phenoconverters, each copy of the protective allele G was associated with a significant reduction in the rate of serum NfL accumulation (‐5.33 pg/mL/year; p = 7.79 × 10 −9 ). Structural imaging analyses revealed decreased rates of atrophy in fronto‐orbital regions and the insular cortex among protective allele carriers. Cognitive trajectories showed significantly slower decline across multiple domains including general cognition (MMSE; p = 0.003), attention and processing speed ( p = 2.2 × 10 −4 ), executive function ( p = 2.6 × 10 −7 ), language ( p = 2.9 × 10 −3 ), and behavioural symptoms as measured by CBI ( p = 9.5 × 10 −3 ). Conclusion The TMEM106B‐rs1990622 protective variant significantly modulates disease progression in genetic FTD phenoconverters across multiple markers, suggesting its potential as a therapeutic target.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.024
GPT teacher head0.292
Teacher spread0.268 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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