Evidence map›Paper›PMID 37964953›Full record

ArticleFrontiers in endocrinology2023

From normal population to prediabetes and diabetes: study of influencing factors and prediction models.

Di Gong, Xiaohong Chen, Lin Yang, Yongjian Zhang, Qianqian Zhong, Jing Liu, Chen Yan, Yongjiang Cai, Weihua Yang, Jiantao Wang

Abstract read
In one paragraph

Article in Frontiers in endocrinology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed, 1 pooled it
–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

10 citing papers in PubMed, 1 synthesis or guideline 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

10 authors.

Di GongShenzhen Eye Hospital, Jinan University, Shenzhen, Guangdong, China.
Xiaohong ChenCenter of Health Management, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China.
Lin YangCenter of Health Management, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China.
Yongjian ZhangCenter of Health Management, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China.
Qianqian ZhongCenter of Health Management, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China.
Jing LiuCenter of Health Management, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China.
Chen YanShenzhen Eye Hospital, Jinan University, Shenzhen, Guangdong, China.
Yongjiang CaiCenter of Health Management, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China.
Weihua YangShenzhen Eye Hospital, Jinan University, Shenzhen Eye Institute, Shenzhen, Guangdong, China.
Jiantao WangShenzhen Eye Hospital, Jinan University, Shenzhen Eye Institute, Shenzhen, Guangdong, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: The purpose of this study is to investigate the independent influencing factors of the transition from normal population to prediabetes, and from prediabetes to diabetes, and to further construct clinical prediction models to provide a basis for the prevention and management of prediabetes and diabetes. Materials and methods: The data for this study were based on clinical information of participants from the Health Management Center of Peking University Shenzhen Hospital. Participants were classified into normal group, prediabetes group, and diabetes group according to their functional status of glucose metabolism. Spearman's correlation coefficients were calculated for the variables, and a matrix diagram was plotted. Further, univariate and multivariate logistic regression analysis were conducted to explore the independent influencing factors. The independent influencing factors were used as predictors to construct the full-variable prediction model (Full.model) and simplified prediction model (Simplified.model). Results: This study included a total of 5310 subjects and 22 variables, among which there were 1593(30%) in the normal group, 3150(59.3%) in the prediabetes group, and 567(10.7%) in the diabetes group. The results of the multivariable logistic regression analysis showed that there were significant differences in 9 variables between the normal group and the prediabetes group, including age(Age), body mass index(BMI), systolic blood pressure(SBP), urinary glucose(U.GLU), urinary protein(PRO), total protein(TP), globulin(GLB), alanine aminotransferase(ALT), and high-density lipoprotein cholesterol(HDL-C). There were significant differences in 7 variables between the prediabetes group and the diabetes group, including Age, BMI, SBP, U.GLU, PRO, triglycerides(TG), and HDL.C. The Full.model and Simplified.model constructed based on the above influencing factors had moderate discriminative power in both the training set and the test set. Conclusion: Age, BMI, SBP, U.GLU, PRO, TP, and ALT are independent risk factors, while GLB and HDL.C are independent protective factors for the development of prediabetes in the normal population. Age, BMI, SBP, U.GLU, PRO, and TG are independent risk factors, while HDL.C is an independent protective factor for the progression from prediabetes to diabetes. The Full.model and Simplified.model developed based on these influencing factors have moderate discriminative power.

Indexed as

Diabetes MellitusPrediabetic StateBlood GlucoseHumansRisk FactorsTriglyceridesBlood GlucoseTriglyceridesdiabetesinfluencing factorsodds ratio (OR)prediabetesprediction model

Identifiers

PMID37964953
PMCPMC10640999

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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.