Evidence map›Paper›PMID 40135648›Full record

ArticleJournal of diabetes2025

The Relationship Between Glycated Albumin and Time in Tight Range in Type 2 Diabetes.

Jiaying Ni, Wenshuo Han, Yaxin Wang, Jiamin Yu, Wei Lu, Yufei Wang, Xiaojing Ma, Jingyi Lu, Jian Zhou

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of diabetes, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

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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

9 authors.

Jiaying NiDepartment of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine; Shanghai Clinical Center for Diabetes, Shanghai Diabetes Institute; Shanghai Key Laboratory of Diabetes Mellitus, Shanghai, China.
Wenshuo HanDepartment of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine; Shanghai Clinical Center for Diabetes, Shanghai Diabetes Institute; Shanghai Key Laboratory of Diabetes Mellitus, Shanghai, China.
Yaxin WangDepartment of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine; Shanghai Clinical Center for Diabetes, Shanghai Diabetes Institute; Shanghai Key Laboratory of Diabetes Mellitus, Shanghai, China.
Jiamin YuDepartment of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine; Shanghai Clinical Center for Diabetes, Shanghai Diabetes Institute; Shanghai Key Laboratory of Diabetes Mellitus, Shanghai, China.
Wei LuDepartment of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine; Shanghai Clinical Center for Diabetes, Shanghai Diabetes Institute; Shanghai Key Laboratory of Diabetes Mellitus, Shanghai, China.
Yufei WangDepartment of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine; Shanghai Clinical Center for Diabetes, Shanghai Diabetes Institute; Shanghai Key Laboratory of Diabetes Mellitus, Shanghai, China.
Xiaojing MaDepartment of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine; Shanghai Clinical Center for Diabetes, Shanghai Diabetes Institute; Shanghai Key Laboratory of Diabetes Mellitus, Shanghai, China.ORCID https://orcid.org/0000-0001-9607-161X
Jingyi LuDepartment of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine; Shanghai Clinical Center for Diabetes, Shanghai Diabetes Institute; Shanghai Key Laboratory of Diabetes Mellitus, Shanghai, China.ORCID https://orcid.org/0000-0003-1105-4189
Jian ZhouDepartment of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine; Shanghai Clinical Center for Diabetes, Shanghai Diabetes Institute; Shanghai Key Laboratory of Diabetes Mellitus, Shanghai, China.ORCID https://orcid.org/0000-0002-1534-2279

Funding

Noncommunicable Chronic Diseases-National Science and Technology Major Project 2024ZD0532001Sanming Project of Medicine in Shenzhen SZSM202311019Shanghai Leading Talent Program of Eastern Talent Plan LJ2024121Shanghai Oriental Talent Program (Youth Project)Shenzhen Medical Research Fund C2406002
6 · The paper itself

Abstract

aimsAmong the new glucose metrics derived from continuous glucose monitoring, the concept of time in tight range (TITR) has gained increasing attention. We aimed to assess the association between TITR and traditional glycemic indicators, such as glycated albumin (GA).

methodsA total of 310 patients with type 2 diabetes on a stable glucose-lowering regimen over the previous 3 months were enrolled. TITR and time in range (TIR) were calculated using continuous glucose monitoring data collected over a minimum of 5 days. Spearman correlation analysis was performed to assess the relationships between traditional glycemic indicators, including GA and HbA1c, with TITR and TIR. Receiver operating characteristic curves were used to evaluate the predictive value of GA for TITR > 50% and TIR > 70%.

resultsThe median levels of GA and HbA1c were 15.6% (14.0%, 17.3%) and 6.5% (6.1%, 7.1%), respectively. Median TITR and TIR were 70.0% (56.0%, 81.0%) and 91.0% (84.0%, 96.8%), respectively. Spearman correlation analysis showed a moderate negative relationship between GA and both TITR and TIR. The optimal GA cutoff for identifying either TITR > 50% or TIR > 70% was 17.4%. Moreover, combining GA with fasting plasma glucose or 2-h postprandial glucose significantly enhanced the ability to identify TITR > 50%, achieving performance comparable to the combination of HbA1c and plasma glucose.

conclusionsIn patients with type 2 diabetes, a GA cutoff of 17.4% effectively identifies TITR > 50%.

Indexed as

Blood GlucoseDiabetes Mellitus, Type 2Serum AlbuminAgedBiomarkersBlood Glucose Self-MonitoringFemaleGlycated HemoglobinGlycated Serum AlbuminGlycation End Products, AdvancedGlycemic ControlHumansHypoglycemic AgentsMaleMiddle AgedROC CurveBiomarkersBlood GlucoseGlycated HemoglobinGlycated Serum AlbuminGlycation End Products, Advancedhemoglobin A1c protein, humanHypoglycemic AgentsSerum Albuminglycated albumintime in tight rangetype 2 diabetes

Identifiers

PMID40135648
PMCPMC11938112

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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.