Longitudinal Change in Serum Neurofilament Light Chain in Type 2 Diabetes and Early Diabetic Polyneuropathy: ADDITION-Denmark
Bibliographic record
Abstract
Objective To investigate the longitudinal development of neurofilament light chain (NfL) levels in type 2 diabetes with and without diabetic polyneuropathy (+/-DPN) and to explore the predictive potential of NfL as a biomarker for DPN. Research Design and Methods We performed retrospective longitudinal case-control analysis of data from 178 participants of the ADDITION-Denmark cohort of people with screen-detected type 2 diabetes. Biobank samples acquired at the ADDITION-Denmark 5- and 10-year follow-up were analyzed for serum NfL (s-NfL) using single-molecule array and the results were compared to established reference material to obtain NfL z-scores. DPN was diagnosed according to Toronto criteria for confirmed DPN at the 10-year follow-up. Results S-NfL increased over time in +DPN (N=39) and -DPN participants (N=139) at levels above normal age-induced s-NfL increase. Longitudinal s-NfL change was greater in +DPN than in -DPN participants (17.4% [95% CI 4.3; 32.2] or 0.31 SD [95% CI 0.03; 0.60] higher s-NfL or NfL z-score increase in +DPN compared to -DPN). S-NfL at 5-year follow-up was positively associated to nerve conduction studies at 10-year follow-up (p=0.02 to <0.001), but not to DPN risk. AUC’s for s-NfL were not inferior to AUC’s for the Michigan Neuropathy Screening Instrument questionnaire score or vibration detection thresholds. Higher yearly s-NfL increase was associated to higher DPN risk (OR 1.36 [95% CI 1.08; 1.71] per 1 ng/L/year). Conclusions Our findings suggest that preceding s-NfL trajectories differ slightly between those with and without DPN and imply a possible biomarker value of s-NfL trajectories in 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 | 0.002 | 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".