Evidence map›Paper›PMID 39437577›Full record

ArticleThe journal of nutrition, health & aging2024

Impact of diabetes on the association between serum urate levels and incident dementia: a cohort study in the UK biobank.

Yuwei Peng, Lulu Pan, Qiuli Zhu, Ruilang Lin, Chen Huang, Yahang Liu, Yifang Huang, Guochen Li, Ye Yao, Yongfu Yu and 1 more

Abstract read
In one paragraph

Article in The journal of nutrition, health & aging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed, 1 pooled it
–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 synthesis or guideline pooled it.

  1. Pooled it
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.

Yuwei PengDepartment of Biostatistics, Key Laboratory for Health Technology Assessment, National Commission of Health, Key Laboratory of Public Health Safety of Ministry of Education, School of Public Health, Fudan University, Shanghai 200032, China.
Lulu PanDepartment of Biostatistics, Key Laboratory for Health Technology Assessment, National Commission of Health, Key Laboratory of Public Health Safety of Ministry of Education, School of Public Health, Fudan University, Shanghai 200032, China.
Qiuli ZhuHealthcare-Associated Infection Prevention and Control Office, Shanghai General Hospital, Shanghai 200080, China.
Ruilang LinDepartment of Biostatistics, Key Laboratory for Health Technology Assessment, National Commission of Health, Key Laboratory of Public Health Safety of Ministry of Education, School of Public Health, Fudan University, Shanghai 200032, China.
Chen HuangDepartment of Biostatistics, Key Laboratory for Health Technology Assessment, National Commission of Health, Key Laboratory of Public Health Safety of Ministry of Education, School of Public Health, Fudan University, Shanghai 200032, China.
Yahang LiuDepartment of Biostatistics, Key Laboratory for Health Technology Assessment, National Commission of Health, Key Laboratory of Public Health Safety of Ministry of Education, School of Public Health, Fudan University, Shanghai 200032, China.
Yifang HuangDepartment of Biostatistics, Key Laboratory for Health Technology Assessment, National Commission of Health, Key Laboratory of Public Health Safety of Ministry of Education, School of Public Health, Fudan University, Shanghai 200032, China.
Guochen LiDepartment of Epidemiology, Key Laboratory for Health Technology Assessment, National Commission of Health, School of Public Health, Fudan University, Shanghai 200032, China.
Ye YaoDepartment of Biostatistics, Key Laboratory for Health Technology Assessment, National Commission of Health, Key Laboratory of Public Health Safety of Ministry of Education, School of Public Health, Fudan University, Shanghai 200032, China.
Yongfu YuDepartment of Biostatistics, Key Laboratory for Health Technology Assessment, National Commission of Health, Key Laboratory of Public Health Safety of Ministry of Education, School of Public Health, Fudan University, Shanghai 200032, China.
Jianguo TangDepartment of Trauma-Emergency & Critical Care Medicine, Shanghai Fifth People's Hospital, Fudan University, Shanghai 200240, China. Electronic address: tangjianguo@5thhospital.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesDiabetes was associated with increased serum urate levels and a higher risk of dementia. However, current evidence regarding the association between serum urate and dementia is controversial.The research gap on how to effectively control urate levels in the population with diabetes still remains. We aim to examine the association of diabetes status and serum urate with dementia incidence, and the differences in this association among participants with different diabetes statuses.

methodsA total of 321,896 participants was recruited from the UK Biobank and followed up until 2022. Diabetes status was classified into diabetes, prediabetes and normoglycaemia according to the American Diabetes Association 2023 guideline. Serum urate levels were stratified using gender-specific quartiles of concentrations. All-cause dementia, Alzheimer's disease and vascular dementia were ascertained using the International Classification of Diseases-10th revision (ICD-10). Cox proportional hazards regression models were used to examine the association between serum urate, diabetes status, and dementia incidence.

resultsOf the 321,896 participants (mean age, 57 years old; 43.5% males), 7,087 (2.20%) individuals were diagnosed with dementia during the follow-up period. Diabetes was associated with a 70% 58%, and 134% increased risk for all-cause dementia, Alzheimer's disease, and vascular dementia respectively. Elevated serum urate levels were associated with a lower risk of all-cause and cause-specific dementia regardless of the status of diabetes. Each standard deviation increase in urate concentration was related to a 11% reduced risk for all-cause dementia (HR, 0.89; 95% CI, 0.86 to 0.91), 7% for Alzheimer's disease (HR, 0.93; 95% CI, 0.88 to 0.98), and 12% for vascular dementia (HR, 0.88; 95% CI, 0.81 to 0.95).

conclusionAppropriately higher urate levels within the threshold of hyperuricemia can reduce the adverse health effects of excessively high urate levels and better protect the cognitive health of people with varying diabetes status.

Indexed as

Biological Specimen BanksDementiaDiabetes MellitusUric AcidAgedCohort StudiesDementia, VascularFemaleHumansIncidenceMaleMiddle AgedRisk FactorsUK BiobankUnited KingdomUric AcidAlzheimer’s diseaseDiabetesPrediabetesSerum urateVascular dementia

Identifiers

PMID39437577
PMCPMC12877235

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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.