Increased skin autofluorescence predicts future cancer development
Bibliographic record
Abstract
Tissue glycation, assessed through skin autofluorescence (SAF) using an AGE reader, is linked to an increased risk of type 2 diabetes (T2D), cardiovascular disease (CVD), as well as mortality from both CVD and cancer. It was also suggested that higher SAF be linked to a greater incidence of cancer. We aimed to evaluate the relationship between SAF and the time to a new cancer diagnosis in the Lifelines Cohort Study, a population-based study in the Northern Netherlands, in participants with and without T2D. Initial participants' screening was performed between 2006 and 2013. Detailed pathology diagnoses were obtained from the Dutch Nationwide Pathology Databank (Palga) and linked to Lifelines data up to March 2023. We performed Cox proportional hazards analyses adjusted for confounders to investigate the relationship between SAF and cancer incidence. A total of 77,961 participants (75,678 without T2D; 42% males; mean age 43 ± 12 years and 2,283 with T2D; 53% males; mean age 56 ± 12 years), who were free of cancer, were followed for a median of 11.5 years. Cumulative cancer incidence was 10.7% in males and 12.5% in females without diabetes, and 23.6% and 20.2% in males and females with T2D, respectively. SAF was significantly linked to an increased cancer incidence (HR 2.36, 95% CI 2.26-2.45, p < 0.001) in the entire cohort. This relationship was significantly stronger in males than in females (HR 3.13, 95% CI 2.95-3.33, p < 0.001 vs. 1.96, 95% CI 1.86-2.07, p < 0.001). After adjusting for age, sex, waist circumference, body mass index, pack-years of smoking and presence of diabetes and metabolic syndrome, the association between SAF and cancer remained significant (HR 1.11, 95% CI 1.05-1.17, p < 0.001). Sensitivity analyses for incident cancers diagnosed more than 2 years after baseline Lifelines screening and incident cancer after skin cancer exclusion yielded similar results. Especially lung, oesophageal and urinary tract cancer (all p < 0.001) and ovarian, female genital and liver cancer (p < 0.05) were associated with increased SAF. Similarly, higher SAF was linked to increased cancer incidence in people with T2D (HR 1.76, 95% CI 1.50-2.06, p < 0.001). This relationship was no longer statistically significant after adjusting for age and sex (HR 1.07, 95% CI 0.89-1.29) or for all confounders (HR 1.07, 95% CI 0.86-1.32). We conclude that, alongside its previously established application in evaluating the risk of future diabetes, CVD, as well as cause-specific mortality in people without diabetes, higher SAF measurements are linked to a slightly higher incidence of cancer.
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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) | 0.001 | 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".