Evidence map›Paper›PMID 35354619›Full record

ArticleBMJ open2022

Validation of type 2 diabetes subgroups by simple clinical parameters: a retrospective cohort study of NHANES data from 1999 to 2014.

Jing Xie, Hua Shao, Tao Shan, Shenqi Jing, Yaxiang Shi, Junjie Wang, Jie Hu, Yong Li, Ruochen Huang, Naifeng Liu and 1 more

Open access · goldAbstract read
In one paragraph

Article in BMJ open, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
1.4field-weighted citation impact, top 19% 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

3 citing papers in PubMed, 9 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 3 institutions in 1 country.

Jing XieCollege of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, Jiangsu, China.ORCID 0000-0002-8142-3237
Hua ShaoDepartment of Pharmacy, Southeast University Zhongda Hospital, Nanjing, Jiangsu, China.
Tao ShanDepartment of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, Jiangsu, China.
Shenqi JingDepartment of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, Jiangsu, China.
Yaxiang ShiDepartment of Information, Southeast University Zhongda Hospital, Nanjing, Jiangsu, China.
Junjie WangDepartment of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, Jiangsu, China.
Jie HuDepartment of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, Jiangsu, China.
Yong LiDepartment of Cardiology, Jiangsu Province People's Hospital and Nanjing Medical University First Affiliated Hospital, Nanjing, Jiangsu, China.
Ruochen HuangDepartment of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, Jiangsu, China.
Naifeng LiuCollege of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, Jiangsu, China liunf@seu.edu.cn liuyun@njmu.edu.cn.
Yun LiuDepartment of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, Jiangsu, China liunf@seu.edu.cn liuyun@njmu.edu.cn.
Nanjing Medical University · CNChina Pharmaceutical University · CNZhongda Hospital Southeast University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesTo verify whether a simplified method based on age, body mass index (BMI) and glycated haemoglobin (HbA1c) is feasible in classifying patients with type 2 diabetes (T2D), and evaluate the predictive ability of subgroups in several health and mortality outcomes.

designRetrospective cohort study.

settingThe National Health and Nutrition Examination Survey 1999-2014 cycle.

participantsA total of 1960 participants with diabetes and the age at diagnosis greater than 30. PRIMARY AND SECONDARY OUTCOME MEASURES: Participants with T2D were assigned to previously defined (by Ahlqvist) subgroups based on five variables: age, BMI, HbA1c, homoeostasis model assessment (HOMA) 2 estimates of β-cell function (HOMA2-B), and insulin resistance (HOMA2-IR), and on three variables: age, BMI and HbA1c. The classification performances of the three variables were evaluated based on 10-fold cross validation, with accuracy, precision and recall as evaluation criteria. Outcomes were assessed using logistic regression and Cox regression analysis.

resultsWithout HOMA measurements, it is difficult to identify severe insulin-resistant diabetes, but other subgroups can be ideally identified. There is no significant difference between the five variables and the three variables in the ability to predict the prevalence of poor cardiovascular health (CVH), chronic kidney disease, non-alcoholic fatty liver disease and advanced liver fibrosis, and the risk of all-cause, cardiovascular disease and cancer-related mortality (p>0.05), except the prevalence of poor CVH in mild age-related diabetes (p<0.05).

conclusionsA simple classification based on age, BMI and HbA1c could be used to identify T2D with several health and mortality risks, which is accessible in most individuals with T2D. Due to its simplicity and practicality, more patients with T2D can benefit from subgroup specific treatment paradigms.

Indexed as

Diabetes Mellitus, Type 2Insulin ResistanceGlycated HemoglobinHumansNutrition SurveysRetrospective StudiesGlycated Hemoglobindiabetes & endocrinologydiabetic nephropathy & vascular diseasegeneral diabetes

Identifiers

PMID35354619
PMCPMC8969050
OpenAlexW4221132927

What OpenQuestion holds

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LicenceCC BY-NC
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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.