Differential Association of Inflammatory Markers and Growth Factors with Type 2 Diabetes and Polyneuropathy—A Multimarker Approach
Bibliographic record
Abstract
There is emerging evidence supporting a role of inflammation in the development and progression of diabetic sensorimotor polyneuropathy (DSPN) in patients with type 2 diabetes. We aimed to characterize inflammatory signatures associated with DSPN and/or type 2 diabetes using a multimarker approach. We measured 92 serum biomarkers including pro- and anti-inflammatory cytokines, chemokines, and growth factors (GF) using the Proseek Multiplex INF I assay (OLINK Proteomics) in 304 individuals with type 2 diabetes and polyneuropathy, defined by the Toronto Consensus Criteria (2011), from the PROPANE study (DSPN) as well as 158 individuals with type 2 diabetes without DSPN (T2D) and 354 individuals with normal glucose tolerance and without DSPN (CON) from the KORA F4 study (DSPN/T2D/CON [mean±SD]: age: 68±9/71±6/69±5 years; male: 76/59/41%; BMI: 30.8±5.3/30.8±4.4/26.9±3.7 kg/m²; diabetes duration: 13.5±9.6/7.6±5.8/- years; HbA1c: 7.4±1.3/6.6+1.0/5.5+0.3%). After adjustment for multiple testing and sex, age, BMI, and smoking, a biphasic pattern of serum levels (normalized protein expression values) with an increase in T2D and decrease in DSPN was observed for four GFs (e.g., transforming GF (TGF)-α: 4.51±0.51 vs. 4.63±0.56 vs. 3.91±0.51; vascular endothelial GF (VEGF): 10.9±0.5 vs. 11.0±0.5 vs. 10.8±0.6), two chemokines (e.g., C-C motif ligand 4 (CCL4): 8.18±0.57 vs. 8.4±0.6 vs. 7.93±0.68) and one cytokine (oncostatin M: 4.83±0.62 vs. 5.06±0.63 vs. 4.35±0.75). Compared to T2D, the levels of another 12 biomarkers were lower in DSPN (e.g., tumor necrosis factor (TNFSF)-14: 5.65±0.55 vs. 4.90±0.77; matrix metalloproteinase (MMP)-1: 14.4±0.7 vs. 14.1±0.9), while five were higher (e.g., CCL20: 5.2±1.1 vs. 5.79±1.23) (all P<0.00024). In conclusion, deficits in growth factors promoting nerve regeneration/angiogenesis and a complex cross-talk between innate and adaptive immunity may contribute to the development of DSPN in type 2 diabetes. Disclosure G.J. Bönhof: None. A. Strom: None. W. Rathmann: Advisory Panel; Self; AstraZeneca. Research Support; Self; Novo Nordisk A/S. M. Heier: None. C. Meisinger: None. A. Peters: None. M. Roden: Speaker's Bureau; Self; Boehringer Ingelheim GmbH. Research Support; Self; Boehringer Ingelheim GmbH. Consultant; Self; Poxel SA. Research Support; Self; Danone Nutricia Early Life Nutrition, GlaxoSmithKline plc., Nutricia Advanced Medical Nutrition, Sanofi. B. Thorand: None. C. Herder: Other Relationship; Self; Sanofi, Eli Lilly and Company. D. Ziegler: None.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 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 | 0.000 |
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".