Evidence map›Paper›PMID 41498586›Full record

ArticleJournal of diabetes investigation2026

Construction of a prognostic assessment model for diabetic nephropathy based on serum fibrinogen and renal tissue IFTA score.

Lin Ning, Xiaohong Zhang, Yuan Fang, Mengjie Weng, Yongjie Zhuo, Jianxin Wan

Abstract read
In one paragraph

Article in Journal of diabetes investigation, 2026. 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
–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

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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.

  1. Review
4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Lin NingDepartment of Nephrology, Blood Purification Research Center, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China.
Xiaohong ZhangDepartment of Nephrology, Blood Purification Research Center, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China.ORCID https://orcid.org/0000-0003-4119-1715
Yuan FangDepartment of Nephrology, Blood Purification Research Center, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China.ORCID https://orcid.org/0000-0002-6432-603X
Mengjie WengDepartment of Nephrology, Blood Purification Research Center, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China.
Yongjie ZhuoDepartment of Nephrology, Blood Purification Research Center, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China.
Jianxin WanDepartment of Nephrology, Blood Purification Research Center, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China.ORCID https://orcid.org/0000-0002-6733-0472

Funding

Fujian Provincial Health Technology Project 2021CXA018Natural Science Foundation of Fujian Province 2022J01212
6 · The paper itself

Abstract

objectiveTo explore the association between serum fibrinogen (FIB) and clinicopathological features and renal prognosis in type 2 diabetic nephropathy (T2DN), and develop a web-based dynamic model to predict renal progression.

methodsThis paper retrospectively enrolled 173 biopsy-proven T2DN patients and stratified them by the optimal FIB cutoff. Renal progression was defined as a >50% decline in eGFR, doubling of creatinine, or onset of end-stage renal disease (ESRD). Cox regression analysis was used to screen out the independent predictors of T2DN progression; a web-based dynamic prediction model was established and evaluated using time-dependent receiver operating characteristic (Time-ROC) curves, calibration curves, and decision curve analysis (DCA).

resultsOf the 173 patients, 81 (46.82%) experienced the renal endpoint event. Multifactorial Cox regression analysis showed that eGFR, hemoglobin, FIB, parathyroid hormone, and interstitial fibrosis and tubular atrophy (IFTA) scores were independent risk factors for T2DN progression (P < 0.05). Among the three models constructed based on these factors, model 3 had the highest areas under the curve (AUC) (90.42; 95% CI, 85.80-95.04). The AUC of the risk prediction model constructed by the nomogram was 0.846, 0.752, and 0.754 at 1, 3, and 5 years, showing good discrimination, and the Web-based dynamic nomogram demonstrated good calibration.

conclusionsFIB is an independent predictor of T2DN progression. A web-based dynamic model was developed to predict renal progression risk in T2DN.

Indexed as

BiomarkersDiabetes Mellitus, Type 2Diabetic NephropathiesFibrinogenKidneyAtrophyDisease ProgressionFemaleFibrosisGlomerular Filtration RateHumansKidney Failure, ChronicMaleMiddle AgedNomogramsPrognosisBiomarkersFibrinogenPredictive modelRenal progressionType 2 diabetic nephropathy

Identifiers

PMID41498586
PMCPMC12950926

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LicenceCC BY-NC-ND
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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.