The Association between the Severity of Distal Sensorimotor Polyneuropathy and Increased Carotid Atherosclerosis in Individuals with Type 2 Diabetes
Bibliographic record
Abstract
Background: Diabetes contributes to a spectrum of complications encompassing microvascular and macrovascular disorders. This study aimed to explore the correlation between distal sensorimotor polyneuropathy (DSPN) severity and heightened carotid atherosclerosis among individuals with type 2 diabetes mellitus (T2DM). Method: Participants underwent comprehensive assessments including nerve conduction studies (NCS), Toronto Clinical Neuropathy Score (TCNS) evaluations, assessment of cardiometabolic risk factors, and carotid sonography studies covering dynamic and morphological parameters. The resistance index (RI), pulsatility index (PI), peak systolic velocity (PSV), and end-diastolic velocity (EDV) in both the common carotid artery (CCA) and internal carotid artery (ICA), carotid intima-media thickness (IMT), and carotid plaque score (CPS) were also measured. Peripheral nerve function severity was assessed using composite amplitude scores (CAS) derived from NCS. Results: Individuals with DSPN exhibited lower EDV in the CCA and ICA (p < 0.0001 and p = 0.002), higher PI and RI in both CCA and ICA (all p < 0.0001), and higher CPS (p = 0.002). They also demonstrated a higher prevalence of retinopathy as an underlying condition, higher index HbA1c, and reduced estimated glomerular filtration rate (eGFR) (all p < 0.0001). Multiple linear regression analysis revealed significant associations where eGFR, ICA-PI, index HbA1c, waist circumference, and age were correlated with CAS. Meanwhile, diabetes duration, waist circumference, age, and index HbA1c showed significant associations with TCNS. Conclusions: Our study suggests that individuals with T2DM who exhibit more severe carotid atherosclerosis may not only be at increased risk of developing DSPN but also may experience greater severity of DSPN. PI in both the CCA and ICA, along with the CPS, serve as surrogate biomarkers for DSPN severity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".