Evidence map›Paper›PMID 32725485›Full record

ArticleDiabetes therapy : research, treatment and education of diabetes and related disorders2020

Establishment of a Risk Prediction Model for Non-alcoholic Fatty Liver Disease in Type 2 Diabetes.

Yali Zhang, Rong Shi, Liang Yu, Liping Ji, Min Li, Fan Hu

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Diabetes therapy : research, treatment and education of diabetes and related disorders, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed
2.6field-weighted citation impact, top 9% 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

27 citing papers in PubMed, 36 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

6 authors at 1 institution in 1 country.

Yali ZhangSchool of Public Health, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China.
Rong ShiSchool of Public Health, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China.
Liang YuSchool of Public Health, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China.
Liping JiSchool of Public Health, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China.
Min LiSchool of Public Health, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China.
Fan HuSchool of Public Health, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China. joyking2003@163.com.ORCID http://orcid.org/0000-0002-7929-6953
Shanghai University of Traditional Chinese Medicine · CN

Funding

the fourth round of Shanghai Public Health Three-Year Action Plan Key Discipline Construction--Health Education and Health Promotion 15GWZK1002the Youth Research Project from Shanghai Municipal Health and Family Planning Commission 20174Y0083
6 · The paper itself

Abstract

introductionNon-alcoholic fatty liver disease (NAFLD) is becoming more prevalent in patients with type 2 diabetes mellitus (T2DM) and can contribute to serious liver damage in this patient population. The aim of this study was to develop a risk nomogram for NAFLD in a Chinese population with T2DM.

methodsA questionnaire survey, physical examination and biochemical indicator testing were performed on 874 patients with T2DM, and the collected data were used to evaluate the risk to develop NAFLD in T2DM patients. The least absolute shrinkage and selection operator (LASSO) regression analysis method was used to optimize variable selection by running cyclic coordinate descent with k-fold (tenfold in this case) cross-validation. Multivariable logistic regression analysis was applied to build a predictive model by introducing the predictors selected from the LASSO regression analysis. The nomogram was developed based on the selected variables visually. A calibration plot, receiver operating characteristic curve (ROC) and decision curve analysis (DCA) were used to validate the model, with further assessment by external validation.

resultsA total of nine predictors, namely sex, age, total cholesterol (TC), body mass index (BMI), waistline, diastolic blood pressure (DBP), serum uric acid (SUA), course of disease and high-density lipoprotein-cholesterol (HDL-C), were identified by LASSO regression analysis from a total of 24 variables studied. The model constructed using these nine predictors displayed medium prediction ability, with an area under the ROC of 0.848 in the training set and 0.809 in the validation set. The DCA curve showed that the nomogram could be applied clinically if the risk threshold was between 48 and 91%, which was found to be between 44 and 82% in the external validation.

conclusionIntroducing sex, age, TC, BMI, waistline, DBP, SUA, course of disease and HDL-C into the risk nomogram increased its usefulness for predicting NAFLD risk in patients with T2DM.

Indexed as

NomogramNon-alcoholic fatty liver diseaseRisk factorType 2 diabetes mellitus

Identifiers

PMID32725485
PMCPMC7434817
OpenAlexW3045888960

What Socratic holds

Textmetadata
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 Socratic graph.