Association of inflammation and nutrition-based indicators and diabetic foot ulcers: a cross-sectional study and a retrospective study

High NAR, MAR, and RAR were positively associated with the prevalence of DFU, whereas low HALP and PNI were linked with an increased prevalence of DFU. In addition, RAR performed better in terms of predictive ability.

Authors

Institutions

Publication Details

Journal
Frontiers in Endocrinology
Published
2025-09-16
DOI
https://doi.org/10.3389/fendo.2025.1654831
Citations
2
Primary Topic
Diabetic Foot Ulcer Assessment and Management
Type
article
Field-Weighted Citation Impact
2.59

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Association of inflammation and nutrition-based indicators and diabetic foot ulcers: a cross-sectional study and a retrospective study

Hua Chen, Jiezhi Dai, Yu Zhou
2 citations
Frontiers in Endocrinology
Diabetic Foot Ulcer Assessment and Management
2.59
article

Association of inflammation and nutrition-based indicators and diabetic foot ulcers: a cross-sectional study and a retrospective study

Hua Chen, Jiezhi Dai, Yu Zhou
article en
2 citations

Abstract

High NAR, MAR, and RAR were positively associated with the prevalence of DFU, whereas low HALP and PNI were linked with an increased prevalence of DFU. In addition, RAR performed better in terms of predictive ability.

Frontiers in EndocrinologyVol. 16
Shanghai Civil Aviation College (CN), Shanghai Sixth People's Hospital (CN)
Civil Aviation Administration of China
Openalex Percentile: Top 9%
Diabetic Foot Ulcer Assessment and Management
2.59
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.