Association of Non-Dipping Blood Pressure Patterns with Diabetic Peripheral Neuropathy: A Cross-Sectional Study among a Population with Diabetes in Greece
Bibliographic record
Abstract
Diabetic peripheral neuropathy (DPN) is present in 20-50% of cases with diabetes. The pathophysiology of DPN is not yet clear regarding hypertension (HTN). The aim of this study was to assess the association between the stages of DPN and HTN in a Greek population with diabetes. We examined 102 adults for diabetic neuropathy (DPN) from November 2020 to December 2021, using the Toronto Clinical Neuropathy Scale System (TCNSS) to categorize them into two groups (no/mild DPN versus medium/severe DPN). Ambulatory blood pressure monitoring was performed to evaluate their hypertensive status. Univariate and multivariate logistic regression analyses were performed to assess the association between the stage of DPN and HTN. The multivariate analysis, considering sex, age, and dipping status, did not show statistically significant associations between stages of HTN and DPN. However, in contrast to dippers, non-dippers had an almost four-times higher risk of developing medium-to-severe DPN (odds ratio (OR) 3.93; 95% confidence interval (CI) [1.33-11.64]); females, in contrast to males, had a 65% lower risk of developing moderate/severe DPN (OR 0.35; 95%CI [0.14-0.92]). In conclusion, our findings showed no statistically significant associations between DPN and HTN; however, dipping status, hyperglycemia, and female sex were shown to play a role in the pathophysiology of DPN.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".