Influence of depression on genetic predisposition to type 2 diabetes in a multiethnic longitudinal study
Bibliographic record
Abstract
We assessed the association between depression status and prevalent and incident type 2 diabetes (T2D) as well as the interaction between depression and a genetic risk score (GS) based on 20 T2D single-nucleotide polymorphisms (SNPs) in a multi-ethnic longitudinal study. We studied 17,375 participants at risk for dysglycemia. All participants had genotypic and phenotypic data collected at baseline and 9,930 participants were followed-up for a median of 3.3 years. Normal glucose tolerance (NGT), impaired fasting glucose (IFG)/impaired glucose tolerance (IGT) and T2D statuses were determined using an oral glucose tolerance test and the 2003 American Diabetes Association criteria. Depression was diagnosed at baseline using Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM IV). Multivariate logistic regression models were adjusted for age, sex, ethnicity and body-mass index and an interaction term GS X depression was added to the model. After appropriate Bonferroni correction, no significant association between depression and T2D-related traits (IFG/IGT, T2D and dysglycemia), and no significant interaction between the GS and depression status was observed at baseline or follow-up. Our longitudinal data do not support an association between depression and abnormal glycemic status. Moreover, depression does not modify the effect of T2D predisposing gene variants.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".