Evidence map›Paper›PMID 35331213›Full record

ArticleBMC endocrine disorders2022

Predictive performance of lipid parameters in identifying undiagnosed diabetes and prediabetes: a cross-sectional study in eastern China.

Yimin Zhou, Guoping Yang, Chen Qu, Jiaping Chen, Yinan Qian, Lei Yuan, Tao Mao, Yan Xu, Xiaoning Li, Shiqi Zhen and 1 more

Open access · goldAbstract read
In one paragraph

Article in BMC endocrine disorders, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed
4.0field-weighted citation impact, top 5% 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

18 citing papers in PubMed, 31 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

11 authors at 2 institutions in 1 country.

Yimin Zhou *Department of Social Medicine and Health Education, School of Public Health, Nanjing Medical University, 818 Tianyuan East Road, Nanjing, 211166, China.
Guoping Yang *Department of Health Education, Jiangsu Provincial Center for Disease Control and Prevention, 172 Jiangsu Road, Nanjing, 210009, China.
Chen QuDepartment of Health Education, Jiangsu Provincial Center for Disease Control and Prevention, 172 Jiangsu Road, Nanjing, 210009, China.
Jiaping ChenDepartment of Epidemiology, School of Public Health, Nanjing Medical University, 818 Tianyuan East Road, Nanjing, 211166, China.
Yinan QianDepartment of Social Medicine and Health Education, School of Public Health, Nanjing Medical University, 818 Tianyuan East Road, Nanjing, 211166, China.
Lei YuanDepartment of Social Medicine and Health Education, School of Public Health, Nanjing Medical University, 818 Tianyuan East Road, Nanjing, 211166, China.
Tao MaoDepartment of Health Education, Jiangsu Provincial Center for Disease Control and Prevention, 172 Jiangsu Road, Nanjing, 210009, China.
Yan XuDepartment of Health Education, Jiangsu Provincial Center for Disease Control and Prevention, 172 Jiangsu Road, Nanjing, 210009, China.
Xiaoning LiDepartment of Health Education, Jiangsu Provincial Center for Disease Control and Prevention, 172 Jiangsu Road, Nanjing, 210009, China.
Shiqi ZhenDepartment of Health Education, Jiangsu Provincial Center for Disease Control and Prevention, 172 Jiangsu Road, Nanjing, 210009, China. cdczsq@163.com.
Sijun LiuDepartment of Social Medicine and Health Education, School of Public Health, Nanjing Medical University, 818 Tianyuan East Road, Nanjing, 211166, China. sjliu@njmu.edu.cn.
Jiangsu Provincial Center for Disease Control and Prevention · CNNanjing Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDyslipidaemia is a risk factor for abnormal blood glucose. However, studies on the predictive values of lipid markers in prediabetes and diabetes simultaneously are limited. This study aimed to assess the associations and predictive abilities of lipid indices and abnormal blood glucose.

methodsA sample of 7667 participants without diabetes were enrolled in this cross-sectional study conducted in 2016, and all of them were classified as having normal glucose tolerance (NGT), prediabetes or diabetes. Blood glucose, blood pressure and lipid parameters (triglycerides, TG; total cholesterol, TC; high-density lipoprotein cholesterol, HDL-C; low-density lipoprotein cholesterol, LDL-C; non-high-density lipoprotein cholesterol, non-HDL-C; and triglyceride glucose index, TyG) were evaluated or calculated. Logistic regression models were used to analyse the association between lipids and abnormal blood glucose. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve was used to assess the discriminatory power of lipid parameters for detecting prediabetes or diabetes.

resultsAfter adjustment for potential confounding factors, the TyG was the strongest marker related to abnormal blood glucose compared to other lipid indices, with odds ratios of 2.111 for prediabetes and 5.423 for diabetes. For prediabetes, the AUCs of the TG, TC, HDL-C, LDL-C, TC/HDL-C, TG/HDL-C, non-HDL-C and TyG indices were 0.605, 0.617, 0.481, 0.615, 0.603, 0.590, 0.626 and 0.660, respectively, and the cut-off points were 1.34, 4.59, 1.42, 2.69, 3.39, 1.00, 3.19 and 8.52, respectively. For diabetes, the AUCs of the TG, TC, HDL-C, LDL-C, TC/HDL-C, TG/HDL-C, non-HDL-C and TyG indices were 0.712, 0.679, 0.440, 0.652, 0.686, 0.692, 0.705, and 0.827, respectively, and the cut-off points were 1.35, 4.68, 1.42, 2.61, 3.44, 0.98, 3.13 and 8.80, respectively.

conclusionsThe TyG, TG and non-HDL-C, especially TyG, are accessible biomarkers for screening individuals with undiagnosed diabetes.

Indexed as

Diabetes MellitusPrediabetic StateBiomarkersChinaCholesterol, HDLCholesterol, LDLCross-Sectional StudiesHumansTriglyceridesBiomarkersCholesterol, HDLCholesterol, LDLTriglyceridesDiabetesLipidPrediabetesPredictive

Identifiers

PMID35331213
PMCPMC8952267
OpenAlexW4221065054

What Socratic holds

Textmetadata
LicenceCC BY
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 Socratic graph.