Evidence map›Paper›PMID 41890164›Full record

ArticleGeneral psychiatry2026

Dementia risk prediction in individuals with prediabetes or diabetes: A novel multi-protein score with biological pathway analysis.

Yuanyuan Zhang, Yu Huang, Junyu Xue, Yiwei Zhang, Sisi Yang, Yanjun Zhang, Ziliang Ye, Xiaoqin Gan, Yiting Wu, Fanfan Hou and 1 more

Abstract read
In one paragraph

Article in General psychiatry, 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

11 authors.

Yuanyuan ZhangDivision of Nephrology Nanfang Hospital Southern Medical University National Clinical Research Center for Kidney and Urological Diseases State Key Laboratory of Multi-Organ Injury Prevention and Treatment Guangdong Provincial Institute of Nephrology Guangdong Provincial Key Laboratory of Renal Failure Research Guangzhou Guangdong China.
Yu HuangDivision of Nephrology Nanfang Hospital Southern Medical University National Clinical Research Center for Kidney and Urological Diseases State Key Laboratory of Multi-Organ Injury Prevention and Treatment Guangdong Provincial Institute of Nephrology Guangdong Provincial Key Laboratory of Renal Failure Research Guangzhou Guangdong China.
Junyu XueDepartment of Endocrinology and Metabolism Nanfang Hospital Southern Medical University Guangzhou Guangdong China.
Yiwei ZhangDivision of Nephrology Nanfang Hospital Southern Medical University National Clinical Research Center for Kidney and Urological Diseases State Key Laboratory of Multi-Organ Injury Prevention and Treatment Guangdong Provincial Institute of Nephrology Guangdong Provincial Key Laboratory of Renal Failure Research Guangzhou Guangdong China.
Sisi YangDivision of Nephrology Nanfang Hospital Southern Medical University National Clinical Research Center for Kidney and Urological Diseases State Key Laboratory of Multi-Organ Injury Prevention and Treatment Guangdong Provincial Institute of Nephrology Guangdong Provincial Key Laboratory of Renal Failure Research Guangzhou Guangdong China.
Yanjun ZhangDivision of Nephrology Nanfang Hospital Southern Medical University National Clinical Research Center for Kidney and Urological Diseases State Key Laboratory of Multi-Organ Injury Prevention and Treatment Guangdong Provincial Institute of Nephrology Guangdong Provincial Key Laboratory of Renal Failure Research Guangzhou Guangdong China.
Ziliang YeDivision of Nephrology Nanfang Hospital Southern Medical University National Clinical Research Center for Kidney and Urological Diseases State Key Laboratory of Multi-Organ Injury Prevention and Treatment Guangdong Provincial Institute of Nephrology Guangdong Provincial Key Laboratory of Renal Failure Research Guangzhou Guangdong China.
Xiaoqin GanDivision of Nephrology Nanfang Hospital Southern Medical University National Clinical Research Center for Kidney and Urological Diseases State Key Laboratory of Multi-Organ Injury Prevention and Treatment Guangdong Provincial Institute of Nephrology Guangdong Provincial Key Laboratory of Renal Failure Research Guangzhou Guangdong China.
Yiting WuDivision of Nephrology Nanfang Hospital Southern Medical University National Clinical Research Center for Kidney and Urological Diseases State Key Laboratory of Multi-Organ Injury Prevention and Treatment Guangdong Provincial Institute of Nephrology Guangdong Provincial Key Laboratory of Renal Failure Research Guangzhou Guangdong China.
Fanfan HouDivision of Nephrology Nanfang Hospital Southern Medical University National Clinical Research Center for Kidney and Urological Diseases State Key Laboratory of Multi-Organ Injury Prevention and Treatment Guangdong Provincial Institute of Nephrology Guangdong Provincial Key Laboratory of Renal Failure Research Guangzhou Guangdong China.
Xianhui QinDivision of Nephrology Nanfang Hospital Southern Medical University National Clinical Research Center for Kidney and Urological Diseases State Key Laboratory of Multi-Organ Injury Prevention and Treatment Guangdong Provincial Institute of Nephrology Guangdong Provincial Key Laboratory of Renal Failure Research Guangzhou Guangdong China.ORCID https://orcid.org/0000-0001-7812-7982

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Individuals with prediabetes or diabetes face elevated dementia risk, yet robust prediction tools and mechanistic insights remain limited. Aims: This study aimed to develop and validate a protein-based risk score for dementia prediction in this high-risk population while elucidating underlying biological pathways and therapeutic targets. Methods: Utilising data from 10 433 UK Biobank participants with prediabetes or diabetes and proteomic profiling (2911 plasma proteins measured), we developed a dementia protein risk score in a training set ( Results: In the training set, 23 out of 2911 proteins were selected. In the testing set, compared with the basic model (age and sex, C-index: 0.78; 95% confidence interval [CI] 0.74-0.82), the dementia protein risk score (C-index: 0.84; 95% CI 0.81-0.88) significantly improved the performance in predicting incident dementia (C-index increase: 0.06; 95% CI 0.02-0.12), while cardiovascular risk factors, ageing and dementia incidence risk factors (C-index: 0.80; 95% CI 0.76-0.83) and apolipoprotein E (APOE; age and sex included, C-index: 0.81; 95% CI 0.77-0.85) had no significant improvement. Six key proteins (glial fibrillary acidic protein [GFAP], neurofilament light polypeptide [NEFL], Brevican core protein [BCAN], protein MENT [MENT], APOE and growth/differentiation factor 15 [GDF15]) captured the most predictive power. Pathway analyses implicated extracellular matrix remodelling and cholesterol metabolism, whereas Mendelian randomisation identified causal roles for APOE, haematopoietic prostaglandin D synthase (HPGDS), BAG family molecular chaperone regulator 3 (BAG3) and GDF15. Nine proteins were prioritised as druggable targets, including HPGDS, with existing Food and Drug Administration-approved drugs. Conclusions: This study establishes a highly accurate protein-based risk score for dementia prediction (including 6-23 proteins) in individuals with prediabetes or diabetes, uncovering actionable biological pathways and therapeutic targets. The findings enable precision risk stratification and accelerate translational opportunities for dementia prevention in this population.

Indexed as

dementiadiabetesdrug targetsMendelian randomisation analysisproteomics

Identifiers

PMID41890164
PMCPMC13015835

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

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.