Multimodal testing reveals subclinical neurovascular dysfunction in prediabetes, challenging the diagnostic threshold of diabetes
Bibliographic record
Abstract
Abstract Aim To explore if novel non‐invasive diagnostic technologies identify early small nerve fibre and retinal neurovascular pathology in prediabetes. Methods Participants with normoglycaemia, prediabetes or type 2 diabetes underwent an exploratory cross‐sectional analysis with optical coherence tomography angiography (OCT‐A), handheld electroretinography (ERG), corneal confocal microscopy (CCM) and evaluation of electrochemical skin conductance (ESC). Results Seventy‐five participants with normoglycaemia ( n = 20), prediabetes ( n = 29) and type 2 diabetes ( n = 26) were studied. Compared with normoglycaemia, mean peak ERG amplitudes of retinal responses at low (16‐Td·s: 4.05 μV, 95% confidence interval [95% CI] 0.96–7.13) and high (32‐Td·s: 5·20 μV, 95% CI 1.54–8.86) retinal illuminance were lower in prediabetes, as were OCT‐A parafoveal vessel densities in superficial (0.051 pixels/mm 2 , 95% CI 0.005–0.095) and deep (0.048 pixels/mm 2 , 95% CI 0.003–0.093) retinal layers. There were no differences in CCM or ESC measurements between these two groups. Correlations between HbA 1c and peak ERG amplitude at 32‐Td·s ( r = −0.256, p = 0.028), implicit time at 32‐Td·s ( r = 0.422, p < 0.001) and 16‐Td·s ( r = 0.327, p = 0.005), OCT parafoveal vessel density in the superficial ( r = −0.238, p = 0.049) and deep ( r = −0.3, p = 0.017) retinal layers, corneal nerve fibre length (CNFL) ( r = −0.293, p = 0.017), and ESC‐hands ( r = −0.244, p = 0.035) were observed. HOMA‐IR was a predictor of CNFD ( β = −0.94, 95% CI −1.66 to −0.21, p = 0.012) and CNBD ( β = −5.02, 95% CI −10.01 to −0.05, p = 0.048). Conclusions The glucose threshold for the diagnosis of diabetes is based on emergent retinopathy on fundus examination. We show that both abnormal retinal neurovascular structure (OCT‐A) and function (ERG) may precede retinopathy in prediabetes, which require confirmation in larger, adequately powered studies.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".