Association of climatic determinants with Type 1 and Type 2 Diabetes worldwide: Night length and photoperiod variation linked to T1D and sunshine to T2D
Bibliographic record
Abstract
Abstract Nearly 500 million individuals are affected by diabetes worldwide. This very high prevalence is combined with a North-South gradient and a seasonality of diagnostics which all suggest the role of climate in diabetes etiology. However, only little is known about the impact of climate on diabetes. This article aims to understand the association of climatic variables with type 1 and type 2 diabetes (T1D and T2D) for 72 countries worldwide (1989-2021). T1D is, on average, more prevalent at extreme latitudes whereas T2D prevalence is higher near equator ( P < 0,001). Sunshine, temperature, solar irradiance and daylength (photoperiod) are negatively associated with T1D prevalence and positively associated with T2D in simple regression ( P < 0,001). Multicollinearity of climatic variables is considered as a challenge, and it is assessed with VIF and optimized with multiple regression. After adjustment, only photoperiod is associated with T1D prevalence (r 2 =0,45) and sunshine with T2D prevalence (r 2 =0,48). T1D monthly incidences are approximated with a cosine regression (RR=1,53) which is significantly associated with photoperiod along the year in Europe ( P < 0,05). The relation between photoperiod and T1D has never been reported before in an ecological study and a short review is developed in the discussion.
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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 | 0.000 | 0.001 |
| 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) | 0.003 | 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".