Evidence map›Paper›PMID 42147096›Full record

ArticleFrontiers in endocrinology2026

A novel QTc-RR differential biomarker for the early assessment of autonomic dysfunction in type 2 diabetes.

Xuwei Liao, Shanglin Yang, Hsientsai Wu, Yuyang Lin, Hongbin Zhou, Juin J Liou

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In one paragraph

Article in Frontiers in endocrinology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper 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

6 authors.

Xuwei LiaoSchool of Electrical and Information Engineering, North Minzu University, Yinchuan, Ningxia, China.
Shanglin Yang *School of Electrical and Information Engineering, North Minzu University, Yinchuan, Ningxia, China.
Hsientsai Wu *School of Electrical and Information Engineering, North Minzu University, Yinchuan, Ningxia, China.
Yuyang LinSchool of Electrical and Information Engineering, North Minzu University, Yinchuan, Ningxia, China.
Hongbin ZhouSchool of Electrical and Information Engineering, North Minzu University, Yinchuan, Ningxia, China.
Juin J LiouSchool of Electrical and Information Engineering, North Minzu University, Yinchuan, Ningxia, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: Prolonged QT intervals are clinically relevant in patients with type 2 diabetes mellitus (T2DM). However, accurately detecting the Q and T points in electrocardiogram (ECG) signals remains challenging owing to the variability in T-wave morphology, and few engineering solutions have effectively addressed this issue. Methods: We analysed 30-min Lead II ECG recordings from 88 patients with T2DM and 93 healthy controls. A novel automated algorithm was developed to accurately detect the Q and T points, accommodating five classified T-wave morphological types. Two electrophysiologists verified the accuracy of the algorithm. Following validation, the QTc intervals derived from the algorithm were compared between the groups. Patients with T2DM exhibited significantly prolonged QTc values and a higher incidence of long QT patterns. A new parameter, RQT Results: The proposed method accurately detected the Q and T points across diverse ECG morphologies and classified the T-wave patterns. QTc values derived from this method significantly distinguished T2DM patients from controls. The novel parameter, RQT Conclusion: This interdisciplinary study presents an engineering-based solution for robust Q- and T-point detections. The novel RQT

Indexed as

Autonomic Nervous System DiseasesBiomarkersDiabetes Mellitus, Type 2ElectrocardiographyHeart RateAgedAlgorithmsCase-Control StudiesFemaleHumansMaleMiddle AgedROC CurveBiomarkersagingautonomic dysfunctionentropy analysisheart rate variabilityQTc interval–RR interval differentialtype 2 diabetes mellitus

Identifiers

PMID42147096
PMCPMC13173515

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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.