The interaction effect between <scp>BMI</scp> , diabetes and age at diabetes onset on the risk of thyroid cancer: A population‐based cohort study in Shanghai, China
Bibliographic record
Abstract
Abstract Aim To determine the association of the presence of diabetes and, among persons with diabetes, the age at type 2 diabetes mellitus (T2DM) onset, BMI and the interactive effect with the subsequent thyroid cancer risk. Materials and Methods We conducted a population register‐based longitudinal cohort study in Shanghai, including 428 568 persons with new‐onset T2DM matched with the general population. The risk of thyroid cancer among subgroups was calculated based on standardized incidence ratio (SIR), hazard ratio (HR) and Cox proportional hazards models. Results In total, 1142 thyroid cancer cases were identified during 8 years of follow‐up, with an incidence rate of 59.01/100 000 person‐years and a higher risk (SIR = 1.21) compared with the general population. The earlier age at T2DM onset and higher BMI were associated with an increasing risk of thyroid cancer independently (onset age <50, SIR: 1.46; BMI ≥30.0 kg/m 2 , SIR: 1.93), with the highest risk in patients with both BMI ≥30.0 kg/m 2 and onset age <50 years (SIR = 3.91, HR = 3.04). Among patients with T2DM onset age <60 years, SIR increased with higher BMI, while there were no trends when onset age ≥60 years. Among patients with BMI ≥25.0 kg/m 2 , SIR increased with an earlier onset age, whereas no trends were shown in the BMI <24.9 kg/m 2 groups. Obese (BMI ≥30.0 kg/m 2 ) patients had a significantly higher HR of thyroid cancer only when T2DM onset age <60 years. Conclusions Both earlier age of T2DM onset (<50 years) and higher BMI (≥30 kg/m 2 ) contributed to the higher risk of thyroid cancer. Patients with young‐onset T2DM and obesity are considered more vulnerable to thyroid cancer development.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 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 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".