Evidence map›Paper›PMID 42118476›Full record

ArticleGeroScience2026

Age-adjusted neurofilament light chain cutoffs for diagnosing neurodegenerative dementia: a two-threshold machine learning approach.

Chiara Gallingani, Riccardo Maramotti, Chiara Carbone, Giulia Vinceti, Silvia Cossutti, Najara Iacovino, Daniela Ballotta, Simone Salemme, Teresa Urbano, Roberta Bedin and 3 more

Abstract read
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In one paragraph

Article in GeroScience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

13 authors.

Chiara Gallingani *Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, 41125, Modena, Italy. chiara.gallingani@unimore.it.ORCID http://orcid.org/0000-0001-5057-0181
Riccardo Maramotti *Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, 41125, Modena, Italy.
Chiara CarboneDepartment of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, 41125, Modena, Italy.
Giulia VincetiNeurology Unit, OCB Hospital, Azienda Ospedaliero-Universitaria of Modena, 41126, Modena, Italy.
Silvia CossuttiDepartment of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, 41125, Modena, Italy.
Najara IacovinoDepartment of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, 41125, Modena, Italy.
Daniela BallottaDepartment of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, 41125, Modena, Italy.
Simone SalemmeDepartment of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, 41125, Modena, Italy.
Teresa UrbanoEnvironmental, Genetics, and Nutritional Epidemiology Research Center (CREAGEN), Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, 41125, Modena, Italy.
Roberta BedinDepartment of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, 41125, Modena, Italy.
Annalisa ChiariNeurology Unit, OCB Hospital, Azienda Ospedaliero-Universitaria of Modena, 41126, Modena, Italy.
Giovanna ZamboniDepartment of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, 41125, Modena, Italy.
Manuela TondelliDepartment of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, 41125, Modena, Italy.

Funding

HORIZON EUROPE European Research Council 101042625
6 · The paper itself

Abstract

Clinical implementation of neurofilament light chain (NfL), a biomarker of neurodegeneration, remains challenging due to absence of reliable cutoffs and influence of confounding factors, particularly age. We aimed to develop an age-adjusted, two-threshold classification framework to support clinical interpretation of NfL in the neurodegenerative dementia diagnosis. We retrospectively enrolled subjects with cognitive/behavioral disturbances and cognitively unimpaired controls (CTR). Participants underwent a baseline diagnostic workup, including at least a neuropsychological assessment, blood test, CSF and serum NfL measurement, and MRI, and were followed up for 2 years. At follow-up, they were diagnosed with either a neurodegenerative dementia [Alzheimer's Disease (AD), Frontotemporal Dementia (FTD), or Lewy Body Dementia (LBD)] or a non-neurodegenerative condition [Other Etiology (OE)]. AD, FTD, and LBD were grouped as Neurodegenerative (NDG), while OE and CTR were grouped as Non-Neurodegenerative (non-NDG). Bayesian regression models assessed the effects of age, disease duration, renal function, and gender on NfL. A weighted support vector machine (SVM) with leave-one-out cross-validation defined age-adjusted cutoffs using a two-threshold strategy constraining sensitivity ≥ 85% and limiting the intermediate zone. We included 217 subjects (158 NDG, 59 non-NDG). Serum and CSF NfL levels were significantly higher in NDG. Age strongly increased NfL levels, particularly in serum where diagnostic separation declined beyond age 70. The SVM-based model applied to CSF NfL defined continuous, age-adjusted, two-threshold cutoffs, identifying low-risk, high-risk, and intermediate zones. The proposed framework for CSF NfL provides a clinically oriented decision-support tool to stratify the likelihood of underlying neurodegeneration and guide diagnostic workup in cognitive neurology settings, pending external validation.

Indexed as

CutoffsDiagnosisNeurodegenerative dementiaNeurofilament light chain

Identifiers

PMID42118476

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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.