Contemporary Prevalence of Diabetic Neuropathy in Type 1 Diabetes (T1D)—Findings from the T1D Exchange
Bibliographic record
Abstract
Diabetic peripheral neuropathy (DPN) is a major cause of disability, mortality and poor quality of life in patients with T1D, with prior reported prevalence rates of up to 35%. The contemporary prevalence of DPN in T1D patients was evaluated in T1D Exchange Registry centers throughout the United States. The Michigan Neuropathy Screening Instrument (MNSI), a validated 15-item self-administered questionnaire, was used to assess DPN in adults ≥18 years with ≥ 5 years of T1D duration. A score of ≥4 was used to define DPN. Diabetes-related characteristics and laboratory data were obtained through the most recent clinic update. Chi-square and t-tests were used to compare demographic and diabetes-related characteristics between those with and without DPN. Linear regression was used to determine the effect of DPN on HbA1c, adjusted for possible confounders. In preliminary analyses of 5,058 participants across 62 sites (mean age 39±18 years, T1D duration 22±14 years, 56% female, 88% non-hispanic white, mean HbA1c 8.1±1.6%), the prevalence of DPN was 10%. Those with DPN were older (52±17 vs. 37±18 years), more likely to be female (61% vs. 55%), had longer T1D duration (32±16 vs. 21±13 years), lower annual household income (37% vs. 59% earning ≥$75K), and lower education level (55% vs. 69% with college degree) than those without DPN (all p<0.001). They also had higher systolic blood pressure (126±17 vs. 123±14 mmHg), triglycerides (117±89 vs. 95±62 mg/dL), tobacco use (9% vs. 4%) and prevalence of established CVD (26% vs. 6%), despite higher use of CVD-modifying agents such as statins (64% vs. 31%) and ACE-inhibitors/ARBs (45% vs. 23%) (all p<0.001). Participants with DPN had higher HbA1c (8.4±1.7% vs. 8.1±1.6%), even after adjusting for multiple confounders (p <0.01). The prevalence of DPN in this national T1D cohort is lower than prior published reports, reflecting current clinical care practices, and highlighting other non-glycemic risk factors for DPN including CVD risk factors and socioeconomic status. Disclosure K.R. Mizokami-Stout: None. C.T. Boyle: None. V.N. Shah: None. G. Aleppo: Research Support; Self; AstraZeneca, Novo Nordisk Inc.. Consultant; Self; Dexcom, Inc.. Advisory Panel; Self; Novo Nordisk Inc. J.B. McGill: Research Support; Self; AstraZeneca. Consultant; Self; Boehringer Ingelheim GmbH. Speaker's Bureau; Self; Aegerion Pharmaceuticals. Consultant; Self; Bayer AG, Dexcom, Inc., Intarcia Therapeutics, Inc.. Speaker's Bureau; Self; Janssen Pharmaceuticals, Inc., MannKind Corporation. Research Support; Self; Novartis Pharmaceuticals Corporation. Consultant; Self; Novo Nordisk A/S. R. Pratley: Other Relationship; Self; AstraZeneca, Boehringer Ingelheim Pharmaceuticals, Inc., Eisai Inc., GlaxoSmithKline plc., Janssen Pharmaceuticals, Inc., Lexicon Pharmaceuticals, Inc., Ligand Pharmaceuticals, Inc., Eli Lilly and Company, Merck & Co., Inc., Novo Nordisk Inc., Pfizer Inc., Sanofi-Aventis, Takeda Development Center Americas, Inc.. E. Toschi: None. L. Ang: None. R. Pop-Busui: Research Support; Self; AstraZeneca.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".