321-OR: Differential Associations of Obesity and Metabolic Syndrome with Polyneuropathy in Early Type 2 Diabetes and Normal Glucose Tolerance
Bibliographic record
Abstract
There is increasing evidence for a role of the metabolic syndrome (MetS) during development of distal sensorimotor polyneuropathy (DSPN). We hypothesized that MetS components are differentially associated with measures of DSPN in individuals with normal glucose tolerance (NGT) and early type 2 diabetes (T2D). Electrophysiological and quantitative sensory testing was performed in 180 participants with NGT and 355 with recent-onset T2D (known diabetes duration <1 year) matched for age from the German Diabetes Study (NGT/T2D [mean±SD]: age: 43.9±14.0/45.1±7.7 years; sex: 61/65% male; BMI: 26.8±5.0/32.3±6.5 kg/m²; HbA1c: 5.2±0.3/6.5±1.0%). Intraepidermal nerve fiber density (IENFD) was assessed in 218 individuals (NGT/T2D: n=117/101). Subsets of participants were followed for 5 years (NGT/T2D: n=48/53). MetS was defined according to International Diabetes Federation (2006) and DSPN according to Toronto consensus criteria (2011). After adjustment for age, sex, height, smoking and HbA1c, a lower IENFD was associated with higher weight (β=-0.306), number of MetS components (NMetSC; β=-0.222) and presence of central obesity (β=-0.292), while higher malleolar vibration perception threshold was associated with weight (β=0.219) in NGT (P<0.05), but not in T2D. Median motor nerve conduction velocity and sural sensory nerve action potential were inversely associated with weight (β=-0.182/-0.286), NMetSC (β=-0.127/-0.128), presence of central obesity (β=-0.163/-0.165), and MetS (β=-0.169/-0.203) in T2D (P<0.05). Higher weight predicted the presence of DSPN at 5 years (β=0.652, P=0.001) in NGT, while increasing NMetSC and MetS prevalence predicted a decline in IENFD over 5 years (β=-0.533/-0.388, P=0.001/0.013) in T2D. This suggests that obesity indices can predict DSPN over 5 years in NGT, while MetS and its components are differentially associated with measures of DSPN in early, well-controlled type 2 diabetes compared with NGT. Disclosure G.Sipola: None. A.Strom: None. M.Bombrich: None. R.Wagner: Advisory Panel; Daiichi Sankyo, Speaker's Bureau; Novo Nordisk, Sanofi. D.Ziegler: None. M.Roden: Advisory Panel; Eli Lilly and Company, Consultant; TARGET PharmaSolutions, Inc., Research Support; Boehringer-Ingelheim, Novo Nordisk, Novartis, Sanofi. G.J.Bönhof: None. Gds group: n/a. Funding German Federal Ministry of Health; Ministry of Innovation, Science, Research and Technology of the state North Rhine-Westphalia; German Federal Ministry of Education and Research
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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 | 0.001 |
| 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".