Evidence map›Paper›PMID 37497611›Full record

ArticleThe Journal of clinical endocrinology and metabolism2023

Associations of Glucose Metabolism Status with Brain Macrostructure and Microstructure: Findings from the UK Biobank.

Ruyi Li, Tingting Geng, Lin Li, Qi Lu, Rui Li, Xue Chen, Yunjing Ou, Sen Liu, Xiaoyu Lin, Qingying Tian and 6 more

Open access · hybridAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in The Journal of clinical endocrinology and metabolism, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
1.1field-weighted citation impact, top 21% of its field
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

5 citing papers in PubMed, 5 citations in OpenAlex.

  1. Review
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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

16 authors at 3 institutions in 1 country.

Ruyi LiDepartment of Nutrition and Food Hygiene, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Tingting GengDepartment of Nutrition and Food Hygiene, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.ORCID 0000-0002-6220-3968
Lin LiDepartment of Nutrition and Food Hygiene, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Qi LuDepartment of Nutrition and Food Hygiene, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.ORCID 0000-0002-0385-5712
Rui LiDepartment of Nutrition and Food Hygiene, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Xue ChenDepartment of Nutrition and Food Hygiene, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Yunjing OuDepartment of Nutrition and Food Hygiene, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Sen LiuDepartment of Nutrition and Food Hygiene, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Xiaoyu LinDepartment of Nutrition and Food Hygiene, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Qingying TianDepartment of Nutrition and Food Hygiene, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Zixin QiuDepartment of Nutrition and Food Hygiene, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Kai ZhuDepartment of Nutrition and Food Hygiene, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Ziyue TangDepartment of Nutrition and Food Hygiene, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Kun YangDepartment of Endocrinology, Affiliated Dongfeng Hospital, Hubei University of Medicine, Shiyan, China.
An PanDepartment of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.ORCID 0000-0002-1089-7945
Gang LiuDepartment of Nutrition and Food Hygiene, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.ORCID 0000-0002-1430-3016
Huazhong University of Science and Technology · CNFudan University · CNHubei University of Medicine · CN

Funding

Fundamental Research Funds for the Central Universities 2021GCRC076Hubei Province Science Fund for Distinguished Young Scholars 2021CFA048National Natural Science Foundation of China 82273623
6 · The paper itself

Abstract

contextEvidence linking glucose metabolism status with brain macro- and microstructure is limited and inconsistent.

objectiveWe aim to investigate the associations of glucose metabolism status with brain macrostructure and microstructure, including brain volumes, subcortical gray matter volumes, and white matter microstructural metrics.

methodsThis study enrolled 29 251 participants from the UK Biobank. Glucose metabolism status was classified into normal glucose metabolism (NGM), prediabetes, type 2 diabetes (T2D) with HbA1c <7%, and T2D with HbA1c ≥7%. Brain macrostructural metrics included volumes of total and subcortical gray matter, white matter, white matter hyperintensity (WMH), cerebrospinal fluid, and brain stem. Brain microstructural metrics included fractional anisotropy (FA) and mean diffusivity in white matter tracts. Multivariable linear regression models were used to estimate β values and 95% CI.

resultsAfter multivariable adjustment including demographic and lifestyle factors, medical history, and total intracranial volume, those with prediabetes had smaller total and subcortical gray matter volumes than participants with NGM, while atrophy of total and subcortical gray matter was more pronounced in those with T2D (all P trend < .05). Moreover, participants with T2D had larger volumes of white matter and WMH (both P trend < .05). For brain microstructure, participants with prediabetes had lower FA values in commissural fibers (β -0.04; 95% CI -0.08, -0.003). Global and tract-specific microstructural abnormalities of white matter were observed in participants with T2D, especially for T2D with HbA1c ≥ 7% (all P trend < .05), except for FA values in projection fibers.

conclusionThese findings suggest that interventions for hyperglycemia at an earlier stage may help protect brain health.

Indexed as

Diabetes Mellitus, Type 2Prediabetic StateWhite MatterBiological Specimen BanksBrainDiffusion Tensor ImagingGlucoseGlycated HemoglobinHumansUK BiobankGlucoseGlycated Hemoglobindiffusion tensor imaginggray mattermagnetic resonance imagingprediabetestype 2 diabeteswhite matter

Identifiers

PMID37497611
PMCPMC12186999
OpenAlexW4385279971

What OpenQuestion holds

Textmetadata
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.