Evidence map›Paper›PMID 34163192›Full record

ArticleDiabetes, metabolic syndrome and obesity : targets and therapy2021

Nomogram Predicting the Risk of Progression from Prediabetes to Diabetes After a 3-Year Follow-Up in Chinese Adults.

Kai Liang, Xinghong Guo, Chuan Wang, Fei Yan, Lingshu Wang, Jinbo Liu, Xinguo Hou, Wenjuan Li, Li Chen

Open access · goldAbstract read
In one paragraph

Article in Diabetes, metabolic syndrome and obesity : targets and therapy, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 2 pooled it
1.9field-weighted citation impact, top 13% 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

7 citing papers in PubMed, 2 syntheses or guidelines pooled it, 15 citations in OpenAlex.

  1. Pooled it
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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.

Kai LiangDepartment of Endocrinology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, 250012, People's Republic of China.
Xinghong GuoDepartment of Endocrinology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, 250012, People's Republic of China.
Chuan WangDepartment of Endocrinology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, 250012, People's Republic of China.
Fei YanDepartment of Endocrinology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, 250012, People's Republic of China.
Lingshu WangDepartment of Endocrinology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, 250012, People's Republic of China.
Jinbo LiuDepartment of Endocrinology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, 250012, People's Republic of China.
Xinguo HouDepartment of Endocrinology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, 250012, People's Republic of China.
Wenjuan LiDepartment of Endocrinology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, 250012, People's Republic of China.
Li ChenDepartment of Endocrinology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, 250012, People's Republic of China.
Qilu Hospital of Shandong University · CNShandong University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeTo develop a nomogram for predicting the risk of progression from prediabetes to diabetes and provide a quantitative predictive tool for early clinical screening of high-risk populations of diabetes. MATERIALS AND

methodsThis study was a retrospective cohort study and part of the investigation conducted for the Risk Evaluation of cAncers in Chinese diabeTic Individuals: a lONgitudinal (REACTION) study. A total of 1857 prediabetic participants at baseline underwent oral glucose tolerance test and hemoglobin A1c (HbA1c) testing after 3 years. The areas under the receiver operating characteristic curves (AUCs) were adopted to measure the predictive value of progression to diabetes, using baseline fasting plasma glucose (FPG), 2-hr postprandial plasma glucose (2hPG), HbA1c or combined models. Decision curve analysis determined the model with the best discriminative ability. A nomogram was formulated and internally validated, providing an individualized predictive tool by calculating total scores.

resultsAfter 3 years, 145 participants developed diabetes, and the annual incidence was estimated to be 2.60%. Among the three single indicators and four combined models, model 4 combined of FPG, 2hPG, and HbA1c showed the best performance in risk predication, with an AUC of 0.742. The nomogram constructed via model 4 was validated and demonstrated good prediction for the risk of diabetes. The nomogram score/predicted probability was a numeric value representing the prediction model score of individual patients. Notably, all nomogram scores showed relatively high negative predictive values.

conclusionThe nomogram constructed in this study effectively predicts and quantifies the risk of progression from prediabetes to diabetes after a 3-year follow-up and could be adopted to identify Chinese patients at high risk for diabetes in order to provide timely interventions.

Indexed as

diabetesnomogramprediabetespredictive value

Identifiers

PMID34163192
PMCPMC8214014
OpenAlexW3166073883

What OpenQuestion holds

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LicenceCC BY-NC
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.