Childhood trauma, depression, and the risk of incident prediabetes in young adults: findings from the Lifelines Cohort Study
Bibliographic record
Abstract
Abstract Background Childhood trauma and depression have been shown to increase the risk of type 2 diabetes. However, many studies have focused on middle-age and older adults, with less known on the role of these variables in early glucose dysregulation. The goal of the study was to examine childhood trauma, depression, and their interactions, as risk factors for the onset of prediabetes in young adults. Methods Data were from the Dutch Lifelines Cohort Study. N = 8,650 adults (61% female) between 18-35 years without prediabetes/diabetes at baseline (2007-2014) were included. Childhood trauma was assessed using the Childhood Trauma Questionnaire. Depression was assessed using the Mini International Neuropsychiatric Interview. Prediabetes at follow-up (2014-2017) was considered by haemoglobin A1c levels between 5.7%-6.4%. Logistic regressions examined associations between depression and childhood trauma with the risk of incident prediabetes. Odds ratios (OR) and 95% confidence intervals (CI) for unadjusted analyses and analyses adjusted for age, sex, education, ethnicity, body mass index, smoking, and alcohol use (reduced adjusted sample size; n = 7,186) are presented. Results 244 participants (2.8%) developed prediabetes. In univariate analyses, childhood trauma (OR = 1.02, CI = 1.01-1.03, p=.006) and depression (OR = 2.08, CI = 1.01-4.29, p=.048) predicted incident prediabetes. When childhood trauma subscales were examined, only sexual abuse significantly predicted incident prediabetes. In adjusted analyses, only childhood trauma, specifically sexual abuse, significantly predicted incident prediabetes (OR = 1.06, CI = 1.01-1.12, p=.021). No multiplicative interaction between depression and childhood trauma was found. Conclusions Young adults who have experienced childhood trauma, particularly sexual abuse, may be at risk of glucose dysregulation in early adulthood. Early targeted preventive care may help attenuate or halt glucose dysregulation and the development of type 2 diabetes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".