Evidence map›Paper›PMID 39145313›Full record

ArticleFrontiers in endocrinology2024

Factors influencing accelerated aging in patients with type 2 diabetes mellitus and coronary heart disease.

Zehua Huang, Nana Liu, Shiyi Chen, Zhiren Chen, Peian Wang

Abstract read
In one paragraph

Article in Frontiers in endocrinology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

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3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

5 authors.

Zehua HuangXuzhou Central Hospital, Affiliated Xuzhou Clinical College of Xuzhou Medical University, Xuzhou, Jiangsu, China.
Nana LiuXuzhou Central Hospital, Affiliated Xuzhou Clinical College of Xuzhou Medical University, Xuzhou, Jiangsu, China.
Shiyi ChenXuzhou Central Hospital, Affiliated Xuzhou Clinical College of Xuzhou Medical University, Xuzhou, Jiangsu, China.
Zhiren ChenXuzhou Central Hospital, Affiliated Xuzhou Clinical College of Xuzhou Medical University, Xuzhou, Jiangsu, China.
Peian WangXuzhou Central Hospital, Affiliated Xuzhou Clinical College of Xuzhou Medical University, Xuzhou, Jiangsu, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: To investigate the factors influencing accelerated aging in patients with type 2 diabetes mellitus (T2DM) and coronary heart disease (CHD). Methods: A total of 216 patients diagnosed with T2DM and CHD between August 2019 and August 2023 at Xuzhou Central Hospital were selected. Patients were divided into an aging group and a non-aging group, based on the positive or negative values of phenotypic age acceleration (PhenoAgeAccel). Logistic regression analysis was conducted. Variables that had a univariate analysis Results: This study included 216 patients, with 89 in the accelerated aging group, and 127 in the non-accelerated aging group. The average age of patients was 70.40 (95% CI: 69.10-71.69) years, with 137 males (63.4%). Compared with the non-accelerated aging group, patients in the accelerated aging group were older, with a higher proportion of males, and a higher prevalence of hypertension, stable angina pectoris, and unstable angina pectoris. Multivariate Logistic regression analysis indicated that the absolute value of neutrophils (NEUT#), urea (UREA), adenosine deaminase (ADA), and the triglyceride-glucose index (TyG) were risk factors for accelerated aging, while cholinesterase (CHE) was a protective factor. For each unit increase in NEUT#, UREA, ADA, and TyG, the risk of aging increased by 64%, 48%, 10%, and 789%, respectively. The overall area under the receiver operating characteristic (ROC) curve of the model in the training set was 0.894, with a 95% confidence interval (CI) of 0.851-0.938. Conclusion: NEUT#, CHE, UREA, ADA, and TyG are predictors of accelerated aging in patients with T2DM and CHD, with the model showing favorable overall predictive performance.

Indexed as

Coronary DiseaseDiabetes Mellitus, Type 2Adenosine DeaminaseAgedAgingAging, PrematureChinaFemaleHumansMaleMiddle AgedRisk FactorsTriglyceridesUreaAdenosine DeaminaseTriglyceridesUreaADAcoronary artery diseaseinfluencing factorsphenotypic age accelerationTyGtype 2 diabetes mellitus

Identifiers

PMID39145313
PMCPMC11322350

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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.