Clinical and Technical Factors Associated with Skin Intrinsic Fluorescence in Subjects with Type 1 Diabetes from the Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications Study
Bibliographic record
Abstract
BACKGROUND: The Diabetes Control and Complications Trial (DCCT)/Epidemiology of Diabetes Interventions and Complications(EDIC) studies have established multiyear mean hemoglobin A1c (HbA1c) as predictive of microvascular complications in persons with type 1 diabetes. However, multiyear mean HbA1c is not always available in the clinical setting. Skin advanced glycation end products (AGEs) are thought to partially reflect effects of hyperglycemia over time, and measurement of skin AGEs might be a surrogate for multiyear mean HbA1c. As certain AGEs fluoresce and skin fluorescence has been demonstrated to correlate with the concentration of skin AGEs, noninvasive measurement by skin intrinsic fluorescence(SIF) facilitates the exploration of the association of mean HbA1c and other clinical/technical factors with SIF using the detailed phenotypic database available in DCCT/EDIC. SUBJECTS AND METHODS: Of the subjects, 1,185 (53% male) had measurements of SIF during years 16/17 of EDIC with mean age and diabetes duration of 51.5 and 29.8 years, respectively. SIF measurements were obtained on the underside of the forearm near the elbow using a skin fluorescence spectrometer. Demographic data and health history were self-reported, and an annual standardized examination measured clinical status. Linear regression models were constructed to identify significant clinical and technical factors associated with SIF, and the final models only used factors that were significant. RESULTS: SIF ranged from 8.7 to 54.0 arbitrary units and was log-normally distributed. Log(SIF) correlated more with mean HbA1c as the time period increased. In multivariate analyses log(SIF) was significantly associated with mean HbA1c, age,estimated glomerular filtration rate < 60mL/min/m2, smoking status, skin tone, and clinic latitude <37 N. CONCLUSIONS: SIF reflects age, mean HbA1c over time, smoking, and renal damage, which are known risk factors for diabetes complications.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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".