Evidence map›Paper›PMID 41667997›Full record

ArticleBMC neurology2026

Development of a clinical nomogram to assess the risk of cognitive impairment in community-dwelling middle-aged and older adults.

Mengchen Wang, Yao Sun, Xiaoxiao Wang, Chun Liu, Tao Guo, Yu Huang, Frankliu Gao, Bensheng Qiu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC neurology, 2026. 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

8 authors.

Mengchen WangDepartment of Medical Imaging Technology, School of Medical Imaging, Bengbu Medical University, Bengbu, Anhui, 233030, China.
Yao SunSchool of Medical Information Engineering, Gannan Medical University, Ganzhou, Jiangxi, China.
Xiaoxiao WangDepartment of Electronic Engineering and Information Science, Medical Imaging Center, University of Science and Technology of China, Hefei, Anhui, 230026, China.
Chun LiuDepartment of Medical Imaging Technology, School of Medical Imaging, Bengbu Medical University, Bengbu, Anhui, 233030, China.
Tao GuoDepartment of Electronic Engineering and Information Science, Medical Imaging Center, University of Science and Technology of China, Hefei, Anhui, 230026, China. taoguo@mail.ustc.edu.cn.
Yu HuangDepartment of Medical Imaging Technology, School of Medical Imaging, Bengbu Medical University, Bengbu, Anhui, 233030, China. yuhuang@ustc.edu.cn.
Frankliu GaoMichael G. Foster School of Business, University of Washington, Box 353200, Seattle, WA, 98195-3200, USA.
Bensheng QiuDepartment of Medical Imaging Technology, School of Medical Imaging, Bengbu Medical University, Bengbu, Anhui, 233030, China.

Funding

National Natural Science Foundation of China 32427801Yangtze River Delta Science and Technology Innovation Community Joint Basic Research Project of China 2024CSJZN1200
6 · The paper itself

Abstract

backgroundCognitive impairment is a prevalent condition among middle-aged and older adults and often progresses to dementia, posing substantial clinical and societal burdens. Early assessment of high-risk individuals is essential for timely intervention and management. This study aimed to develop a practical nomogram for the assessment of cognitive impairment in community-dwelling elderly populations. METHODOLOGY: This cross-sectional study recruited 581 participants between October 23 and November 8, 2023, comprising 465 assigned to the training cohort and 116 to the validation cohort. Demographic information, medical history, lifestyle, and biochemical parameters were collected using structured questionnaires. Cognitive impairment was assessed via the Montreal Cognitive Assessment (MoCA). Independent features were identified using LASSO regression followed by binary logistic regression, and a nomogram was constructed based on these variables. Model performance was evaluated by discrimination, calibration, and clinical utility using Receiver Operating Characteristic (ROC) curves, calibration plots, the Hosmer–Lemeshow test, and Decision Curve Analysis (DCA).

resultsCognitive impairment prevalence was 38.5% in the training and 32.8% in the validation cohort. Six features—sex, age, systolic blood pressure, homocysteine, fruit consumption, and family history of stroke—were integrated into the nomogram. The model demonstrated good discrimination (AUC 0.816 in training cohort; 0.796 in validation cohort) with satisfactory calibration and clinical applicability.

conclusionThe proposed nomogram provides a reliable and convenient tool for the early risk assessment of cognitive impairment in middle-aged and older adults, facilitating targeted prevention and personalized management in clinical and community settings. Its implementation may assist healthcare professionals in identifying high-risk individuals and mitigating progression toward dementia.

Indexed as

Cognitive DysfunctionIndependent LivingNomogramsAgedCross-Sectional StudiesFemaleHumansMaleMental Status and Dementia TestsMiddle AgedRisk AssessmentRisk FactorsCognitive impairmentMiddle-aged and elderly peopleNomogramRisk factors

Identifiers

PMID41667997
PMCPMC12997881

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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.