Evidence map›Paper›PMID 36576562›Full record

ReviewActa diabetologica2023

Risk prediction of nephropathy by integrating clinical and genetic information among adult patients with type 2 diabetes.

Li-Na Liao, Tsai-Chung Li, Chih-Ching Yeh, Chia-Ing Li, Chiu-Shong Liu, Chuan-Wei Yang, Ya-Fei Yang, Chih-Hsueh Lin, Fuu-Jen Tsai, Cheng-Chieh Lin

Abstract readReview
PubMed Publisher
In one paragraph

Review in Acta diabetologica, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.3field-weighted citation impact, top 42% 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

1 citing paper in PubMed, 2 citations in OpenAlex.

  1. 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

10 authors at 3 institutions in 1 country.

Li-Na Liao *Department of Public Health, College of Public Health, China Medical University, Taichung, Taiwan, R.O.C.
Tsai-Chung Li *Department of Public Health, College of Public Health, China Medical University, Taichung, Taiwan, R.O.C.
Chih-Ching YehDepartment of Public Health, College of Public Health, China Medical University, Taichung, Taiwan, R.O.C.
Chia-Ing LiDepartment of Medical Research, China Medical University Hospital, Taichung, Taiwan, R.O.C.
Chiu-Shong LiuSchool of Medicine, College of Medicine, China Medical University, No. 100, Sec. 1, Jingmao Rd., Beitun Dist., Taichung, 406040, Taiwan, R.O.C.
Chuan-Wei YangDepartment of Medical Research, China Medical University Hospital, Taichung, Taiwan, R.O.C.
Ya-Fei YangDepartment of Nephrology, Everan Hospital, Taichung, Taiwan, R.O.C.
Chih-Hsueh LinSchool of Medicine, College of Medicine, China Medical University, No. 100, Sec. 1, Jingmao Rd., Beitun Dist., Taichung, 406040, Taiwan, R.O.C.
Fuu-Jen TsaiSchool of Chinese Medicine, College of Chinese Medicine, China Medical University, No. 100, Sec. 1, Jingmao Rd., Beitun Dist., Taichung, 406040, Taiwan, R.O.C.. d0704@mail.cmuh.org.tw.
Cheng-Chieh LinDepartment of Medical Research, China Medical University Hospital, Taichung, Taiwan, R.O.C.. cclin@mail.cmuh.org.tw.ORCID http://orcid.org/0000-0002-9625-6216
China Medical University · TWAsia University · TWTaichung Hospital · TW

Funding

China Medical University, Taiwan CMU109-MF-48Ministry of Science and Technology, Taiwan MOST 108-2314-B-039-035-MY3
6 · The paper itself

Abstract

aimsDiabetic nephropathy (DN) is a major healthcare challenge. We developed and internally and externally validated a risk prediction model of DN by integrating clinical factors and SNPs from genes of multiple CKD-related pathways in the Han Chinese population. MATERIALS AND

methodsA total of 1526 patients with type 2 diabetes were randomly allocated into derivation (n = 1019) or validation (n = 507) sets. External validation was performed with 3899 participants from the Taiwan Biobank. We selected 66 SNPs identified from literature review for building our weighted genetic risk score (wGRS). The steps for prediction model development integrating clinical and genetic information were based on the Framingham Heart Study.

resultsThe AUROC (95% CI) for this DN prediction model with combined clinical factors and wGRS was 0.81 (0.78, 0.84) in the derivation set. Furthermore, by directly using the information of these 66 SNPs, our final prediction model had AUROC values of 0.85 (0.82, 0.87), 0.89 (0.86, 0.91), and 0.77 (0.74, 0.80) in the derivation, internal validation, and external validation sets, respectively. Under the combined model, the results with a cutoff point of 30% showed 70.91% sensitivity, 67.84% specificity, 51.54% positive predictive value, and 82.86% negative predictive value.

conclusionsWe developed and internally and externally validated a model with clinical factors and SNPs from genes of multiple CKD-related pathways to predict DN in Taiwan. This model can be used in clinical risk management practice as a screening tool to identify persons who are genetically predisposed to DN for early intervention and prevention.

Indexed as

Diabetes Mellitus, Type 2Diabetic NephropathiesRenal Insufficiency, ChronicAdultGenetic Predisposition to DiseaseHumansRandomized Controlled Trials as TopicRisk FactorsDiabetic nephropathyGenetic risk scorePrediction model

Identifiers

PMID36576562
OpenAlexW4313236312

What OpenQuestion holds

Textmetadata
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 OpenQuestion graph.