Sex Differences in Neuropathy and Neuropathic Pain in Long-Standing Diabetes—Results from the Canadian Study of Longevity in Type 1 Diabetes
Bibliographic record
Abstract
Neuropathy and neuropathic pain are common complications in T1D. We aimed to determine if sex-specific differences in the prevalence of neuropathic pain and neuropathy exist in patients with longstanding T1D. In Phase 1 of the study, 361 Canadians with ≥50 years of T1D completed questionnaires which included subjective assessment for neuropathy defined by Michigan Neuropathy Screening Instrument Questionnaire score ≥3, termed NEUROPATHYMNSI-Q. In Phase 2 of the study, we studied a sub-cohort of 75 diabetes participants and 75 age- and sex-matched nondiabetic controls who completed objective neurological examinations which included assessment of abnormal nerve conduction studies (NCS) for neuropathy, termed NEUROPATHYNCS. In the Phase 1 cohort, more females than males reported neuropathic pain [87(42%) vs. 41(27%); p=0.003)], but the presence of neuropathy (NEUROPATHYMNSI-Q) did not differ by sex [87(42%) females vs. 66(43%) males, p=0.82], and thus neuropathic pain was independent of the presence of neuropathy [adjusted OR for neuropathic pain in females compared to males, 2.7 (1.4-5.0; p=0.002)]. In the Phase 2 participants, neuropathic pain was similar between the sexes (29% females vs. 21% males, p=0.43) while NEUROPATHYNCS was less prevalent among females (83% females vs. 97% males, p=0.05). Though not statistically significant, in a combined analysis of Phase 2 participants adjusted for NEUROPATHYNCS, females had a tendency to a higher adjusted OR for neuropathic pain compared to males [OR 2.0 (95% CI 0.8-4.7), p=0.11]. In conclusion, in patients with longstanding TID, neuropathic pain appears to be greater among females compared to males independent of the presence of neuropathy. Further research using larger datasets with objective neuropathy measures are required to further confirm and address these sex-specific differences. Disclosure N. Cardinez: None. L. Lovblom: None. J. Bai: None. A. Abraham: None. E.J. Lewis: Employee; Self; Nutarniq Corp. D. Scarr: None. J.A. Lovshin: Other Relationship; Self; AstraZeneca. Consultant; Self; Novo Nordisk Inc.. Research Support; Self; Sanofi, Merck Sharp & Dohme Corp.. Other Relationship; Self; Novo Nordisk Inc.. Y. Lytvyn: None. G. Boulet: Advisory Panel; Self; Medtronic, Sanofi, Novo Nordisk Inc.. Other Relationship; Self; Janssen Global Services, LLC., Abbott. M. Farooqi: None. A. Orszag: None. A. Weisman: None. H.A. Keenan: Research Support; Self; Sanofi. Employee; Self; Sanofi Genzyme. M.H. Brent: Research Support; Self; Novartis Canada. Advisory Panel; Self; Novartis Canada. Research Support; Self; Bayer Canada. Advisory Panel; Self; Bayer Canada, Allergan Canada. Research Support; Self; Roche Canada. N. Paul: None. V. Bril: Consultant; Self; Alexion Pharmaceuticals, Inc.. Research Support; Self; CSL Behring, Grifols. Advisory Panel; Self; CSL Behring. Consultant; Self; Grifols. Research Support; Self; Shire. Advisory Panel; Self; Pfizer Inc. D. Cherney: Consultant; Self; AbbVie Inc.. Other Relationship; Self; AstraZeneca, Boehringer Ingelheim GmbH, Eli Lilly and Company. Consultant; Self; Sanofi. Other Relationship; Self; Merck & Co., Inc.. Consultant; Self; Mitsubishi Tanabe Pharma Corporation. Other Relationship; Self; Janssen Pharmaceuticals, Inc. B.A. Perkins: Advisory Panel; Self; Boehringer Ingelheim GmbH. Research Support; Self; Boehringer Ingelheim GmbH, Novo Nordisk Inc.. Advisory Panel; Self; Novo Nordisk Inc., Abbott. Speaker's Bureau; Self; Abbott, Janssen Pharmaceuticals, Inc.. Advisory Panel; Self; Insulet Corporation. Speaker's Bureau; Self; Insulet Corporation, Dexcom, Inc..
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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".