Evidence map›Paper›PMID 40421158›Full record

ArticleDementia & neuropsychologia2025

Pupillary response test as a potential early diagnosis biomarker of Alzheimer's disease.

Robert Shen, Yuda Turana, Yvonne Suzy Handajani, Lukas, Nelly Tina Widjaja, Nova Eka Budiyanta, Reiner Adi Putra Sumarta, Celine Gabriella Wahyudi, Hans Timothy Wijaya, Jimmy Fransisco Abadinta Barus and 3 more

Abstract read
In one paragraph

Article in Dementia & neuropsychologia, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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.

Robert ShenAtma Jaya Catholic University of Indonesia, School of Medicine and Health Sciences, Master's Program in Biomedical Sciences, Atma Jaya Neuroscience Research, North Jakarta, Indonesia.ORCID https://orcid.org/0000-0002-2063-4145
Yuda TuranaAtma Jaya Catholic University of Indonesia, School of Medicine and Health Sciences, Master's Program in Biomedical Sciences, Atma Jaya Neuroscience Research, North Jakarta, Indonesia.ORCID https://orcid.org/0000-0003-4527-0285
Yvonne Suzy HandajaniAtma Jaya Catholic University of Indonesia, Center of Health Research, North Jakarta, Indonesia.ORCID https://orcid.org/0000-0002-8245-9354
LukasAtma Jaya Catholic University of Indonesia, Faculty of Engineering, Cognitive Engineering Research Group, South Tangerang, Indonesia.ORCID https://orcid.org/0000-0001-6546-6555
Nelly Tina WidjajaAtma Jaya Catholic University of Indonesia, Center of Health Research, North Jakarta, Indonesia.ORCID https://orcid.org/0000-0002-3697-0338
Nova Eka BudiyantaAtma Jaya Catholic University of Indonesia, Faculty of Engineering, Cognitive Engineering Research Group, South Tangerang, Indonesia.ORCID https://orcid.org/0000-0003-2861-3426
Reiner Adi Putra SumartaAtma Jaya Catholic University of Indonesia, Faculty of Engineering, Cognitive Engineering Research Group, South Tangerang, Indonesia.ORCID https://orcid.org/0009-0005-1924-3250
Celine Gabriella WahyudiAtma Jaya Catholic University of Indonesia, Faculty of Engineering, Cognitive Engineering Research Group, South Tangerang, Indonesia.ORCID https://orcid.org/0009-0000-1176-6077
Hans Timothy WijayaAtma Jaya Catholic University of Indonesia, Faculty of Engineering, Cognitive Engineering Research Group, South Tangerang, Indonesia.ORCID https://orcid.org/0009-0002-6438-8641
Jimmy Fransisco Abadinta BarusAtma Jaya Catholic University of Indonesia, School of Medicine and Health Sciences, Master's Program in Biomedical Sciences, Atma Jaya Neuroscience Research, North Jakarta, Indonesia.ORCID https://orcid.org/0000-0001-5227-5207
Linda SuryakusumaAtma Jaya Catholic University of Indonesia, School of Medicine and Health Sciences, Department of Neurology, Atma Jaya Neuroscience and Cognitive Center, North Jakarta, Indonesia.ORCID https://orcid.org/0009-0006-3090-976X
Poppy Kristina SasmitaAtma Jaya Catholic University of Indonesia, School of Medicine and Health Sciences, Department of Anatomy, North Jakarta, Indonesia.ORCID https://orcid.org/0000-0002-4859-3402
Tara Puspitarini SaniAtma Jaya Catholic University of Indonesia, School of Medicine and Health Sciences, Department of Neurology, Atma Jaya Neuroscience and Cognitive Center, North Jakarta, Indonesia.ORCID https://orcid.org/0000-0002-8604-5888

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Psychometric tests as the gold-standard for diagnosing Alzheimer's disease (AD) tend to be inefficient, nonreproducible, and are affected by many factors. Research on pupillary response in AD is not novel; however, there has not been a single study providing the diagnostic value of pupillary response parameters as a recommendation for their potential use for early AD diagnosis. Objective: To investigate pupillary response parameters as clinical biomarkers of AD that are dynamic, affordable, efficient, and objective (non-operator dependent). Methods: A cross-sectional study on pupillary response parameters was conducted, with comparative and correlation-analytical tests between pupillary response and the Mini-Mental State Examination cognitive score. The diagnostic value of pupillary response parameters was obtained by determining the cutoff point of each parameter by calculating the sensitivity, specificity, Youden's maximum index, Area Under the Curve (AUC) value from the Receiver Operating Characteristic (ROC) curve, positive predictive value, and negative predictive value. Pupillary response parameters were classified into four types: size, response time, velocity, and acceleration. Results: There were 128 respondents in total, consisting of cognitively-unimpaired young adults, cognitively-unimpaired older adults, and cognitively-impaired older adults. The pupillary response values without involving acceleration parameters were reliable. The diagnostic value of change in pupil size, the maximum and the average constriction velocity parameters have normal cognitive cutoff scores of 1.77 mm (sensitivity: 61.3%; specificity: 80%; positive predictive value [PPV]: 47.76%; negative predictive value [NPV]: 86.88%), ≥4.43 mm/s (sensitivity: 70.4%; specificity: 58.5%; PPV: 46.93%; NPV: 78.48%), and 3.12 mm/s (sensitivity: 70.4%; specificity: 55.0%; PPV: 45.83%; NPV: 77.5%), respectively. Conclusions: Pupillary response, in particular changes in constriction and velocity, can be potentially used as an early diagnostic biomarker for AD.

Indexed as

Alzheimer DiseaseBiomarkersDementia

Identifiers

PMID40421158
PMCPMC12105847

What OpenQuestion holds

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Registered trials

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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.