Evidence map›Paper›PMID 38178064›Full record

ArticleBMC pediatrics2024

Characterization of newly diagnosed type 1 diabetes in children and adolescents from 2017 to 2022 in China: a single-center analysis.

Shimin Wu, Yuan Gao, Shusen Guo, Lina Fu, Yanqin Ying, Wei Wu, Ling Hou, Yan Liang, Xiaoping Luo

Open access · goldAbstract read
In one paragraph

Article in BMC pediatrics, 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
2.8field-weighted citation impact, top 11% 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

4 citing papers in PubMed, 5 citations in OpenAlex.

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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 at 2 institutions in 1 country.

Shimin WuDepartment of Pediatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Yuan GaoDepartment of Pediatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Shusen GuoDepartment of Pediatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Lina FuDepartment of Pediatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Yanqin YingDepartment of Pediatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Wei WuDepartment of Pediatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Ling HouDepartment of Pediatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Yan LiangDepartment of Pediatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Xiaoping LuoDepartment of Pediatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China. xpluo@tjh.tjmu.edu.cn.
Tongji Hospital · CNHuazhong University of Science and Technology · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveThis study investigated the characteristics of newly diagnosed type 1 diabetes mellitus (T1DM) related to autoimmunity and the frequency of diabetic ketoacidosis (DKA) in children and adolescents from 2017-2022 in China. RESEARCH DESIGN AND

methodsSingle-center regional data from the Department of Pediatric Endocrinology, Tongji Hospital, were used to compare 88 children and adolescents newly diagnosed with T1DM from 2020 to 2022 (i.e. during the COVID-19 pandemic in China) and 76 children and adolescents diagnosed with T1DM from 2017 to 2019. Auto-antibodies, including glutamic acid decarboxylase-65 and insulin auto-antibodies, were detected by enzyme-linked immunoassays. DKA was defined as a pH < 7.3 and/or a bicarbonate level < 15 mmol/L.

resultsThe median age of the 164 children and adolescents newly diagnosed with T1DM from 2017 to 2022 was 7.0 years (interquartile range [IQR]: 3.8-10.0 years; 51.83% male). The mean annual incidence of T1DM was 2.98 per 1,000,000 child years. The estimated frequency of auto-antibody positivity was 51.22% (n = 84), and there was no difference between the 2020-2022 group and 2017-2019 group (55.68% [n = 49] vs. 46.5% [n = 35]; p = 0.219). The frequency of DKA among the entire cohort was 57.93% (n = 95), and peaked in 2020 at 78.9% (15/19 patients). The frequency of DKA was not significantly higher in the 2020-2022 group compared with the 2017-2019 group (60.23% [n = 53] vs. 55.26% [n = 42]; p = 0.521). We found no significant difference in the frequency of DKA between patients who were negative vs. positive for auto-antibodies in the 2020-2022 group (64.10% [n = 25] vs. 57.14% [n = 28], p > 0.05). The C-peptide level and HbA1c (%) were positively correlated with onset age (R1 = 0.389, p < 0.01; R2 = 0.371, p < 0.01), and the estimated mean C-peptide level was 0.26 ng/ml (IQR: 0.2-0.4 ng/ml) in patients with DKA and 0.370 ng/ml (IQR: 0.2-0.6 ng/ml) in patients without DKA (p = 0.044).

conclusionsThis study showed the annual incidence of T1DM was 2.98 per 1,000,000 child years, gradually increased over the study period, and there was no significant increase in T1DM with auto-antibody positivity in children and adolescents newly diagnosed from 2020-2022 in China compared with the previous 3 years. Furthermore, the frequency of DKA was peaked in 2020, and were not significantly different between patients who were negative vs. positive for auto-antibodies.

Indexed as

Diabetes Mellitus, Type 1Diabetic KetoacidosisAdolescentChildChild, PreschoolC-PeptideFemaleHumansMalePandemicsRetrospective StudiesC-PeptideAutoimmunityChildren and adolescentsCOVID-19KetoacidosisNewly diagnosed type 1 diabetes

Identifiers

PMID38178064
PMCPMC10765937
OpenAlexW4390586870

What OpenQuestion holds

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LicenceCC BY
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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.