Peripheral Neuropathy and Nerve Dysfunction in Individuals at High Risk for Type 2 Diabetes: The PROMISE Cohort
Bibliographic record
Abstract
OBJECTIVE: Emerging evidence suggests that peripheral neuropathy begins in the early stages of diabetes pathogenesis. Our objective was to describe the prevalence of peripheral neuropathy and nerve dysfunction according to glucose tolerance and metabolic syndrome status and examine how these conditions are associated with neurological changes in individuals at risk for type 2 diabetes. RESEARCH DESIGN AND METHODS: We studied 467 individuals in the longitudinal PROMISE (Prospective Metabolism and Islet Cell Evaluation) cohort. Peripheral neuropathy was defined by Michigan Neuropathy Screening Instrument (MNSI) scores (>2), and the severity of nerve dysfunction was measured objectively by vibration perception thresholds (VPTs) using a neurothesiometer. Metabolic syndrome was defined using the International Diabetes Federation/American Heart Association harmonized criteria. RESULTS: The prevalence of peripheral neuropathy was 29%, 49%, and 50% for normal glycemia, prediabetes, and new-onset diabetes, respectively (P < 0.001 for trend). The mean VPT was 6.5 V for normal glycemia, 7.9 V for prediabetes, and 7.6 V for new-onset diabetes (P = 0.024 for trend). Prediabetes was associated with higher MNSI scores (P = 0.01) and VPTs (P = 0.004) versus normal glycemia, independent of known risk factors. Additionally, progression of glucose intolerance over 3 years predicted a higher risk of peripheral neuropathy (P = 0.007) and nerve dysfunction (P = 0.002). Metabolic syndrome was not independently associated with MNSI scores or VPTs. CONCLUSIONS: In individuals with multiple risk factors for diabetes, prediabetes was associated with similar risks of peripheral neuropathy and severity of nerve dysfunction as new-onset diabetes. Prediabetes, but not metabolic syndrome, was independently associated with both the presence of peripheral neuropathy and the severity of nerve dysfunction.
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 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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".