Evidence map›Paper›PMID 40620798›Full record

ArticleFrontiers in endocrinology2025

Combining biomarkers to predict the disease activity of graves' ophthalmopathy: a combinatory model of the NLR, TRAb and FT4.

Tongxin Niu, Lujue Wang, Jing Deng, Yuxian Shi, Yating Liu, Boding Tong, Xin Qi, Dan Cao, Yunping Li

Abstract read
In one paragraph

Article in Frontiers in endocrinology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 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

3 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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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

9 authors.

Tongxin NiuDepartment of Ophthalmology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China.
Lujue WangDepartment of Ophthalmology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China.
Jing DengDepartment of Ophthalmology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China.
Yuxian ShiDepartment of Ophthalmology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China.
Yating LiuDepartment of Ophthalmology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China.
Boding TongDepartment of Ophthalmology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China.
Xin QiDepartment of Ophthalmology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China.
Dan CaoDepartment of Ophthalmology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China.
Yunping LiDepartment of Ophthalmology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: The aim of this study was to identify potential biomarkers associated with active Graves' ophthalmopathy (GO) and develop a model for predicting the occurrence and progression of active GO. Methods: A retrospective study was conducted on 220 GO patients (n=120 in the active phase, n=100 in the stable phase) and 70 healthy controls. Laboratory and other clinical indicators were compared in GO patients at different stages and healthy controls. A multivariate regression analysis model was used to analyze the clinical risk factors affecting the occurrence and progression of active GO, and a predictive model based on risk factors was established. Results: Higher WBC, NEU, NLR, TRAb and FT4 levels (all p<0.005) were detected in patients with active GO than those in inactive GO. The CAS score of the GO patients was positively correlated with the WBC (r = 0.155, p = 0.003), NEU (r = 0.165, p = 0.002), NLR (r = 0.134, p = 0.010), FT3 (r = 0.117, p = 0.031), FT4 (r = 0.139, p = 0.011), and TRAb (r = 0.160, p = 0.004) counts and negatively correlated with the TSH level (r = - 0.114, p = 0.043). Multivariate regression analysis revealed that the NLR, TRAb, and FT4 were significant risk factors for GO progression (all p < 0.05). The cut-off levels for predicting active GO were 8.71 IU/L for TRAb (AUC=0.643, sensitivity 0.58, specificity 0.74), 19.82 ng/dL for FT4 (AUC=0.606, sensitivity 0.41, specificity 0.75), and 2.405 for the NLR (AUC=0.597, sensitivity 0.50, specificity 0.75). A predictive model including these risk factors was built (the cutoff value was 0.6, the AUC was 0.716, the sensitivity was 0.533, and the specificity was 0.808). Conclusion: The laboratory biomarkers for the occurrence and progression of active GO include the NLR, FT4, and TRAb. We have developed a predictive combined model that may assist in timely assessment of GO activity and progression, and provide clues for future development of facile tools to predict GO activity.

Indexed as

BiomarkersGraves OphthalmopathyImmunoglobulins, Thyroid-StimulatingNeutrophilsThyroxineAdultCase-Control StudiesDisease ProgressionFemaleHumansLeukocyte CountMaleMiddle AgedPrognosisRetrospective StudiesRisk FactorsBiomarkersImmunoglobulins, Thyroid-StimulatingThyroxinebiomarkersdisease activityFT4-free tetraiodothyroninegraves' ophthalmopathy-GONLR-neutrophil-to-lymphocyte RatioTRAb-thyrotrophin receptor antibody

Identifiers

PMID40620798
PMCPMC12226272

What Socratic holds

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Registered trials

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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 Socratic graph.