{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T09:53:00Z","timestamp":1792144380634,"version":"4.4.0"},"reference-count":28,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T00:00:00Z","timestamp":1772582400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Endocrinol."],"abstract":"<jats:sec>\n                    <jats:title>Background and Objective<\/jats:title>\n                    <jats:p>Diabetic peripheral neuropathy (DPN) is a prevalent and debilitating complication of type 2 diabetes mellitus (T2DM). Although glycated hemoglobin (HbA1c) is a primary metric for glycemic control, many patients develop or experience progression of DPN despite achieving HbA1c targets, suggesting the importance of other dynamic glycemic parameters. Glycemic variability (GV) may contribute to nerve injury via mechanisms such as oxidative stress, inflammation, and neurotrophic factor dysregulation. However, clinical evidence linking GV to DPN remains inconsistent, and rigorous studies controlling for confounders are scarce. This study aimed to determine whether GV is independently associated with DPN beyond HbA1c in a propensity score-matched (PSM) cohort and to explore the potential mediating roles of inflammatory cytokines and neurotrophic factors.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This single-center retrospective cohort study screened T2DM patients hospitalized between January 1, 2020, and December 31, 2024. Patients with complete 72-hour continuous glucose monitoring (CGM) data and bilateral nerve conduction studies (NCS) were included. DPN was diagnosed according to the Chinese Diabetes Society guidelines. Propensity score matching (PSM, 1:1, caliper=0.02) was used to balance the DPN and non-DPN groups on age, sex, BMI, diabetes duration, HbA1c, systolic blood pressure, LDL-C, and estimated glomerular filtration rate. Primary outcomes included GV parameters (mean amplitude of glycemic excursions [MAGE], coefficient of variation [CV], standard deviation [SD]) and a composite nerve conduction velocity (NCV) Z-score. Serum inflammatory cytokines (IL-6, TNF-\u03b1) and neurotrophic factors (NGF, IGF-1) were measured in a nested subcohort. Data were analyzed using multivariable linear regression, dose-response analysis, causal mediation analysis, and receiver operating characteristic (ROC) curve analysis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>After PSM, 256 well-matched patients (128 in each group) were included, with excellent covariate balance (all standardized mean differences &amp;lt;0.1). GV parameters (MAGE, CV, and SD) remained significantly higher in the DPN group compared to the non-DPN group after matching (all P &amp;lt; 0.001). Within the DPN group, stratification by MAGE tertiles revealed a clear dose-response relationship: higher MAGE tertiles were associated with progressively worse composite NCV Z-scores (P for trend &amp;lt;0.001). Subgroup analysis (n=160) showed that higher MAGE tertiles were associated with elevated IL-6 and TNF-\u03b1 levels and decreased NGF levels (P for trend &amp;lt;0.05). Multivariable linear regression confirmed MAGE (\u03b2 = -0.38, P &amp;lt; 0.001) and CV (\u03b2 = -0.31, P &amp;lt; 0.001) as independent negative predictors of NCV after adjusting for confounders including HbA1c. Mediation analysis indicated that IL-6 and TNF-\u03b1 collectively mediated approximately 32% of the negative effect of MAGE on NCV (indirect effect \u03b2 = -0.12, P &amp;lt; 0.001). ROC curve analysis identified optimal GV thresholds for discriminating DPN: MAGE \u22655.8 mmol\/L (AUC = 0.84, sensitivity 76%, specificity 79%) and CV \u226532.5% (AUC = 0.81, sensitivity 72%, specificity 77%).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>In this propensity score-matched cohort study, higher glycemic variability is independently and robustly associated with the presence and severity of diabetic peripheral neuropathy in patients with T2DM, even after accounting for HbA1c and other conventional risk factors. This association exhibits a dose-response relationship and is partially mediated by systemic inflammation. Our findings advocate for incorporating GV assessment into clinical practice for better DPN risk stratification and suggest that therapeutic strategies aimed at reducing glycemic variability may offer additional neuroprotective benefits.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fendo.2026.1766149","type":"journal-article","created":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T07:04:00Z","timestamp":1772607840000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["From glycemic instability to neuropathic risk: a propensity score-matched retrospective cohort study"],"prefix":"10.3389","volume":"17","author":[{"given":"Chong","family":"Yan","sequence":"first","affiliation":[{"name":"Department of Endocrinology, The Third People\u2019s Hospital of Hubei Province","place":["Wuhan, Hubei, 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