Skin autofluorescence predicts new cardiovascular disease and mortality in people with type 2 diabetes
Bibliographic record
Abstract
BACKGROUND: Skin autofluorescence (SAF) is a non-invasive marker of tissue accumulation of advanced glycation endproducts (AGE). Recently, we demonstrated in the general population that elevated SAF levels predict the development of type 2 diabetes (T2D), cardiovascular disease (CVD) and mortality. We evaluated whether elevated SAF may predict the development of CVD and mortality in individuals with T2D. METHODS: We included 2349 people with T2D, available baseline SAF measurements (measured with the AGE reader) and follow-up data from the Lifelines Cohort Study. Of them, 2071 had no clinical CVD at baseline. 60% were already diagnosed with diabetes (median duration 5, IQR 2-9 years), while 40% were detected during the baseline examination by elevated fasting blood glucose ≥7.0 mmol/l) and/or HbA1c ≥6.5% (48 mmol/mol). RESULTS: . 11% of participants with known T2D were treated with diet, the others used oral glucose-lowering medication, with or without insulin; 6% was using insulin alone. Participants with known T2D had higher SAF than those with newly-detected T2D (SAF Z-score 0.56 ± 0.99 vs 0.34 ± 0.89 AU, p < 0.001), which reflects a longer duration of hyperglycaemia in the former group. Participants with existing CVD and T2D had the highest SAF Z-score: 0.78 ± 1.25 AU. During a median follow-up of 3.7 yrs., 195 (7.6%) developed an atherosclerotic CVD event, while 137 (5.4%) died. SAF was strongly associated with the combined outcome of a new CVD event or mortality (OR 2.59, 95% CI 2.10-3.20, p < 0.001), as well as incidence of CVD (OR 2.05, 95% CI 1.61-2.61, p < 0.001) and death (OR 2.98, 2.25-3.94, p < 0.001) as a single outcome. In multivariable analysis for the combined endpoint, SAF retained its significance when sex, systolic blood pressure, HbA1c, total cholesterol, eGFR, as well as antihypertensive and statin medication were included. In a similar multivariable model, SAF was independently associated with mortality as a single outcome, but not with incident CVD. CONCLUSIONS: Measuring SAF can assist in prediction of incident cardiovascular disease and mortality in individuals with T2D. SAF showed a stronger association with future CVD events and mortality than cholesterol or blood pressure levels.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".