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 Anglo-Danish-Dutch Study of Intensive Treatment in People with Screen-Detected Diabetes in Primary Care-Denmark (ADDITION-Denmark) cohort of people with screen-detected type 2 diabetes. Biobank samples acquired at the ADDITION-Denmark 5- and 10-year follow-ups were analyzed for serum NfL (s-NfL) using single-molecule array, and the results were compared with 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 with -DPN). s-NfL at the 5-year follow-up was positively associated with nerve conduction studies at the 10-year follow-up (P = 0.02 to <0.001), but not with DPN risk. Areas under the curve (AUCs) for s-NfL were not inferior to AUCs for the Michigan Neuropathy Screening Instrument questionnaire score or vibration detection thresholds. Higher yearly s-NfL increase was associated with higher DPN risk (odds ratio 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".