Evidence map›Paper›PMID 40862120›Full record

ArticleFrontiers in endocrinology2025

Red blood cell count and its inverse association with diabetic retinopathy: Exploratory development of a risk assessment model in a retrospective cohort.

Jing Li, Qian Xu, Xiao Yuan, Wen Hu

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Article in Frontiers in endocrinology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Jing LiDepartment of Endocrinology and Metabolism, Suqian First Hospital, Suqian, Jiangsu, China.
Qian XuDepartment of Endocrinology and Metabolism, Suqian First Hospital, Suqian, Jiangsu, China.
Xiao YuanDepartment of Endocrinology and Metabolism, Suqian First Hospital, Suqian, Jiangsu, China.
Wen HuDepartment of Endocrinology and Metabolism, Huai'an Hospital Affiliated to Xuzhou Medical University and Huai'an Second People's Hospital, Huai'an, Jiangsu, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: The purpose of this exploratory study was to investigate the association between red blood cell count (RBC) and diabetic retinopathy (DR) and to develop a preliminary risk assessment framework. Methods: A total of 413 individuals diagnosed with type 2 diabetes mellitus (T2DM) at Suqian First Hospital's Endocrinology Department were included in this study. These participants were divided into training and validation groups in a 7:3 ratio, consisting of 289 and 124 patients respectively. In the training cohort, potential predictive variables were determined through both univariate and multivariate analyses utilizing forward-backward stepwise selection. Only variables with p < 0.05 were included in the nomogram, which encompassed demographic information, clinical laboratory results, and diabetes-associated complications. The performance of the model was evaluated in both groups using receiver operating characteristic (ROC) curve analysis, the Hosmer-Lemeshow test for calibration, and decision curve analysis (DCA) to determine clinical utility. Results: Out of 20 clinical variables examined, five were chosen to develop the nomogram: RBC, serum creatinine (SCR), diabetes duration, diabetic peripheral neuropathy (DPN), and diabetic kidney disease (DKD). The ROC analysis revealed that the area under the curve (AUC) for the training cohort was 0.765 (95% CI 0.709-0.821) and for the validation cohort was 0.707 (95% CI 0.616-0.798). Results from the Hosmer-Lemeshow test were p = 0.233 and p = 0.579, indicating a good fit. The nomogram demonstrated excellent predictive accuracy and provides a quantitative tool for assessing the risk of DR in individuals with T2DM. Conclusion: Our findings suggest an inverse association between RBC levels and DR risk. The exploratory model incorporating RBC provides an initial framework for evaluating DR risk in patients with T2DM. Further validation in prospective cohorts is needed to refine this framework before considering clinical applications.

Indexed as

Diabetes Mellitus, Type 2Diabetic RetinopathyAdultAgedErythrocyte CountFemaleHumansMaleMiddle AgedNomogramsRetrospective StudiesRisk AssessmentRisk FactorsROC Curvediabetic retinopathymodelnomogrampredictionred blood cell countrisk factor

Identifiers

PMID40862120
PMCPMC12375921

What Socratic holds

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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.