Impact of <scp>HbA1c</scp> variability and time‐in‐range fluctuations on large and small nerve fiber dysfunction in well‐controlled type 2 diabetes: A prospective cohort observational study
Bibliographic record
Abstract
AIMS/INTRODUCTION: Glycemic variability (GV) is a critical factor in the development of diabetic sensorimotor polyneuropathy (DSPN). This study aimed to evaluate the association of long-term GV, measured by glycated hemoglobin (HbA1c) average real variability (ARV), and short-term GV, assessed by time-in-range (TIR) ARV, with large and small nerve fiber dysfunction in individuals with well-controlled Type 2 Diabetes (T2D). MATERIALS AND METHODS: A prospective study conducted at a tertiary hospital in Taiwan included 82 T2D participants. Long-term GV was assessed using HbA1c ARV from visit-to-visit measurements at three-month intervals over 1 year. Short-term GV was evaluated as TIR ARV from seven-day fingerstick data collected quarterly. Large and small nerve functions were assessed using the Toronto Clinical Neuropathy Score (TCNS), nerve conduction studies, quantitative thermal testing, and Sudoscan. RESULTS: Linear regression analysis adjusted for age, diabetes duration, and renal function revealed strong correlations between HbA1c ARV, TIR ARV, and diabetes duration. At baseline, high HbA1c ARV and TIR ARV groups exhibited higher TCNS and composite nerve conduction amplitude scores but lower cold detection thresholds compared to the low median groups. At one-year follow-up, TCNS significantly increased in the high HbA1c ARV (P = 0.001) and TIR ARV (P = 0.003) groups compared to the low median groups. CONCLUSIONS: Both long-term and short-term GV significantly contribute to small and large nerve fiber dysfunction in T2D, yielding similar neurological outcomes despite stable mean glucose levels. Combining GV minimization strategies with standard glycemic control may be essential in reducing DSPN risk.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".