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ArticleAlzheimer's research & therapy2024

Can the clinical sign "head-turning sign" and simple questions in "Neucop-Q" predict amyloid β pathology?

Yugaku Daté, Shogyoku Bun, Keisuke Takahata, Masahito Kubota, Yuki Momota, Yu Iwabuchi, Toshiki Tezuka, Hajime Tabuchi, Morinobu Seki, Yasuharu Yamamoto and 12 more

Abstract read
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Article in Alzheimer's research & therapy, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

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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

22 authors.

Yugaku DatéDepartment of Strokology and Neurology, Saiseikai Yokohamashi-Tobu Hospital, 3-6-1 Shimosueyoshi, Tsurumi-ku, Yokohama City, 230-8765, Japan.
Shogyoku BunDepartment of Neuropsychiatry, Keio University School of Medicine, Tokyo, 160-8582, Japan.
Keisuke TakahataDepartment of Neuropsychiatry, Keio University School of Medicine, Tokyo, 160-8582, Japan.
Masahito KubotaDepartment of Neurology, Keio University School of Medicine, Tokyo, 160-8582, Japan.
Yuki MomotaDepartment of Neuropsychiatry, Keio University School of Medicine, Tokyo, 160-8582, Japan.
Yu IwabuchiDepartment of Radiology, Keio University School of Medicine, Tokyo, 160-8582, Japan.
Toshiki TezukaDepartment of Neurology, Keio University School of Medicine, Tokyo, 160-8582, Japan.
Hajime TabuchiDepartment of Neuropsychiatry, Keio University School of Medicine, Tokyo, 160-8582, Japan.
Morinobu SekiDepartment of Neurology, Keio University School of Medicine, Tokyo, 160-8582, Japan.
Yasuharu YamamotoDepartment of Neuropsychiatry, Keio University School of Medicine, Tokyo, 160-8582, Japan.
Ryo ShikimotoDepartment of Neuropsychiatry, Keio University School of Medicine, Tokyo, 160-8582, Japan.
Yu MimuraDepartment of Neuropsychiatry, Keio University School of Medicine, Tokyo, 160-8582, Japan.
Takayuki HoshinoMemory center, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160- 8582, Japan.
Shin KuroseDepartment of Neuropsychiatry, Keio University School of Medicine, Tokyo, 160-8582, Japan.
Sho ShimohamaDepartment of Neurology, Keio University School of Medicine, Tokyo, 160-8582, Japan.
Natsumi SuzukiDepartment of Neuropsychiatry, Keio University School of Medicine, Tokyo, 160-8582, Japan.
Ayaka MorimotoDepartment of Neuropsychiatry, Keio University School of Medicine, Tokyo, 160-8582, Japan.
Azusa OosumiDepartment of Neuropsychiatry, Keio University School of Medicine, Tokyo, 160-8582, Japan.
Yuka HoshinoMemory center, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160- 8582, Japan.
Masahiro JinzakiDepartment of Radiology, Keio University School of Medicine, Tokyo, 160-8582, Japan.
Masaru MimuraDepartment of Neuropsychiatry, Keio University School of Medicine, Tokyo, 160-8582, Japan.
Daisuke ItoDepartment of Neurology, Keio University School of Medicine, Tokyo, 160-8582, Japan. d-ito@jk9.so-net.ne.jp.

Funding

Japan Agency for Medical Research and Development (AMED) JP17pc0101006Japan Society for the Promotion of Science KAKENHI 16H06277
6 · The paper itself

Abstract

backgroundTo establish simple screening tests to suspect Alzheimer's disease (AD) pathology, the clinical sign "head-turning sign" (HTS), which is a patient's behavior of turning their head towards their partner to seek assistance with questions posed by the examiner during the interview, and the simple screening questionnaire for dementia named "Neucop-Q" were validated in participants diagnosed with amyloid and tau positron emission tomography (PET).

methodsWe enrolled 155 patients: 47 cognitive normal, 36 with mild cognitive impairment, 64 with dementia, and 8 with psychiatric disorders. All participants underwent Neucop-Q [three questions: Consciousness/self-awareness of cognitive disabilities (C) normal/impaired (nor/imp), Pleasure/pastime (P) nor/imp, and News/knowledge on current topics (N) nor/imp] and amyloid/tau PET. Additionally, we measured plasma amyloid β (Aβ) 42/40 ratio, phosphorylated tau 181 (pTau181), glial fibrillary acidic protein (GFAP), and neurofilament light (NFL) levels and compared with HTS and Neucop-Q results.

resultsThe specificity and positive predictive value (PPV) of HTS positivity (HTSpos) were the highest (amyloid PET: 0.930 and 0.870, tau PET: 0.944 and 0.957, respectively), while Cimp and Nimp had a high negative predictive value (NPV) for amyloid PET (negativity) (0.750 and 0.725). Pimp showed high specificity for predicting non-AD tau positivity among non-AD participants without amyloid PET positivity (0.854). To validate these findings with PET results, we examined the correlation between well-established AD blood biomarkers and results obtained from these screening tests. HTSpos, Cimp, and Nimp were strongly associated with Aβ42/40 ratio (P < 0.0001, P = 0.0022, and P = 0.001), pTau181 (P < 0.0001, P = 0.0095, and P = 0.001), GFAP (P = 0.0372, P = 0.0088, and P = 0.0002), and amyloid PET Centiloid (P < 0.0001, P = 0.0210, and P = 0.0006), whereas Pimp increased neuroinflammation (GFAP; P = 0.0061) and was associated with non-AD tauopathy. The combination of Neucop-Q questions showed that Cimp/Pnor/Nimp subjects have the highest specificity and PPV (0.972 and 0.833) and were strongly associated with Aβ42/40 ratio (P = 0.0006), pTau181 (P = 0.0006), and amyloid PET Centiloid (P < 0.0001).

conclusionHTSpos, Cimp, and Nimp have diagnostic utility in suspecting MCI due to AD and AD, and Pimp has diagnostic value in non-AD tauopathy. HTSpos, Cimp, and Nimp were associated with biomarkers of Aβ pathology. HTS and Neucop-Q may serve as powerful first-line screening in memory clinics.

trial registrationUMIN Clinical Trials Registry (UMIN-CTR) under registration numbers 000032027 (Registration date: 2018/03/31) and 000030248 (Registration date: 2018/01/01).

Indexed as

Alzheimer DiseaseAmyloid beta-PeptidesPositron-Emission Tomographytau ProteinsAgedAged, 80 and overBiomarkersCognitive DysfunctionFemaleGlial Fibrillary Acidic ProteinHumansMaleMiddle AgedPeptide FragmentsSurveys and QuestionnairesAmyloid beta-PeptidesBiomarkersGlial Fibrillary Acidic ProteinPeptide Fragmentstau ProteinsAlzheimer’s diseaseAmyloid PETBiomarkerDementiaHead-turning signMild cognitive impairmentNeucop-QTau PET

Identifiers

PMID39568089
PMCPMC11580537

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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.