Evidence map›Paper›PMID 33564251›Full record

ArticleDiabetes, metabolic syndrome and obesity : targets and therapy2021

A Non-Invasive Prediction Model for Non-Alcoholic Fatty Liver Disease in Adults with Type 2 Diabetes Based on the Population of Northern Urumqi, China.

Mingyue Xue, Xiaoping Yang, Yuan Zou, Tao Liu, Yinxia Su, Cheng Li, Hua Yao, Shuxia Wang

Abstract 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 11 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed, 2 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

11 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Application and impact of Lasso regression in gastroenterology: A systematic review.Indian journal of gastroenterology : official journal of the Indian Society of Gastroenterology · 2023
    Pooled it
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  11. Anthropometric Indices and Type 2 Diabetes Mellitus as a Risk Factor in Predicting Nonalcoholic Fatty Liver Disease - A Prospective Study in Indian Population.Indian journal of community medicine : official publication of Indian Association of Preventive & Social Medicine
    Article
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

8 authors.

Mingyue XueHospital of Traditional Chinese Medicine Affiliated to the Fourth Clinical Medical College of Xinjiang Medical University, Urumqi 830011, People's Republic of China.
Xiaoping YangHealth Management Institute, Xinjiang Medical University, Urumqi 830011, People's Republic of China.
Yuan ZouHealth Management Institute, Xinjiang Medical University, Urumqi 830011, People's Republic of China.
Tao LiuHealth Management Institute, Xinjiang Medical University, Urumqi 830011, People's Republic of China.
Yinxia SuHealth Management Institute, Xinjiang Medical University, Urumqi 830011, People's Republic of China.
Cheng LiThe First Affiliated Hospital of Xinjiang Medical University, Urumqi 830011, People's Republic of China.
Hua YaoHealth Management Institute, Xinjiang Medical University, Urumqi 830011, People's Republic of China.ORCID 0000-0002-9572-5578
Shuxia WangHealth Management Institute, Xinjiang Medical University, Urumqi 830011, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundHigh prevalence of non-alcoholic fatty liver disease (NAFLD) occurs in type 2 diabetes mellitus (T2DM), and about 13% of diabetic patients eventually die of liver cirrhosis or liver cancer. The purpose of our research was to develop a non-invasive predictive model of NAFLD in adults with T2DM. PATIENTS AND

methodsAdult patients diagnosed with T2DM during physical examination in 2018 in Urumqi were recruited, in total 40,921 cases. We chose questionnaire and physical measurement variables to build a simple, low-cost model. Variables were selected by the least absolute shrinkage and selection operator regression (LASSO). The features chosen by LASSO were used to build the nomogram prediction model of NAFLD. The receiver operating curve (ROC) and calibration were used for model validation.

resultsDeterminants in the nomogram included age, ethnicity, sex, exercise, smoking, dietary ratio, heart rate, systolic blood pressure (SBP), BMI, waist circumference, and atherosclerotic vascular disease (ASCVD). The area under ROC of developing group and validation group was 0.756 (95% confidence interval 0.750-0.761) and 0.755 (95% confidence interval 0.746-0.763), respectively, and the

conclusionA nomogram constructed with accuracy can calculate the possibility of NAFLD in adults with T2DM. If validated externally, this tool could be utilized as a non-invasive method to diagnose non-alcoholic fatty liver in adults with T2DM.

Indexed as

nomogramnon-alcoholic fatty liver diseasescreening tooltype 2 diabetes mellitus

Identifiers

PMID33564251
PMCPMC7866952

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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.