SynthesisFrontiers in public health2023
Development and validation of a risk prediction model for early diabetic peripheral neuropathy based on a systematic review and meta-analysis.
Synthesis in Frontiers in public health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 2 of them syntheses that pooled it.
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Who cites it
14 citing papers in PubMed, 2 syntheses or guidelines pooled it, 13 citations in OpenAlex.
- Prediction model developed on the basis of meta-analysis in the field of medicine: a systematic survey and methodological summaries.BMC medical research methodology · 2025Pooled it
- Cell-derived exosome therapy for diabetic peripheral neuropathy: a preclinical animal studies systematic review and meta-analysis.Stem cell research & therapy · 2025Pooled it
- Identification of potential mechanisms of quercetin in diabetic peripheral neuropathy through integrated network pharmacology and experimental validation.Naunyn-Schmiedeberg's archives of pharmacology · 2026Article
- Plasma Lipoproteins as Key Regulators in Neuropathic Pain: A Comprehensive Review of Mechanisms and Clinical Potential.Pain and therapy · 2026Review
- Predictive value of the fibrinogen-to-high-density lipoprotein cholesterol ratio for sub-clinical diabetic peripheral neuropathy in type 2 diabetes mellitus.Frontiers in aging neuroscience · 2026Article
- Clinical Significance of Serum CCR2 and IDO1 in Diabetic Peripheral Neuropathy.Diabetes, metabolic syndrome and obesity : targets and therapy · 2026Article
- Prevalence of symptomatic polyneuropathy in patients with type 2 diabetes mellitus attending the diabetes clinic at Helen Joseph Tertiary Hospital, South Africa.South African family practice : official journal of the South African Academy of Family Practice/Primary Care · 2025Article
- Machine learning models predict mortality risk in diabetic neuropathy patients using MIMIC-IV data.Scientific reports · 2025Article
- Systematic review and critical appraisal of predictive models for diabetic peripheral neuropathy: Existing challenges and proposed enhancements.World journal of diabetes · 2025Article
- Article
- Gynostemma pentaphyllum (Thunb.) Makino Affects Autophagy and Improves Diabetic Peripheral Neuropathy Through TXNIP-Mediated PI3K/AKT/mTOR Signaling Pathway.Applied biochemistry and biotechnology · 2025Article
- Relationship Between Chronic Inflammatory Indicators and Diabetic Peripheral Neuropathy in Hospitalized Elderly Patients with Type 2 Diabetes.Diabetes, metabolic syndrome and obesity : targets and therapy · 2025Article
- Machine learning-based prediction of diabetic peripheral neuropathy: model development and clinical validation.Frontiers in endocrinology · 2025Article
- Development and external validation of a prediction model for the risk of relapse in psoriasis after discontinuation of biologics.Frontiers in medicine · 2024Article
Corrections and comments
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Early identification and intervention of diabetic peripheral neuropathy is beneficial to improve clinical outcome. Objective: To establish a risk prediction model for diabetic peripheral neuropathy (DPN) in patients with type 2 diabetes mellitus (T2DM). Methods: The derivation cohort was from a meta-analysis. Risk factors and the corresponding risk ratio (RR) were extracted. Only risk factors with statistical significance were included in the model and were scored by their weightings. An external cohort were used to validate this model. The outcome was the occurrence of DPN. Results: A total of 95,604 patients with T2DM from 18 cohorts were included. Age, smoking, body mass index, duration of diabetes, hemoglobin A1c, low HDL-c, high triglyceride, hypertension, diabetic retinopathy, diabetic kidney disease, and cardiovascular disease were enrolled in the final model. The highest score was 52.0. The median follow-up of validation cohort was 4.29 years. The optimal cut-off point was 17.0, with a sensitivity of 0.846 and a specificity of 0.668, respectively. According to the total scores, patients from the validation cohort were divided into low-, moderate-, high- and very high-risk groups. The risk of developing DPN was significantly increased in moderate- (RR 3.3, 95% CI 1.5-7.2, Conclusion: A risk prediction model for DPN including 11 common clinical indicators were established. It is a simple and reliable tool for early prevention and intervention of DPN in patients with T2DM.
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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.