Early life adversity and the comorbidity of psychopathology and cardiometabolic disorders: insights from the UK Biobank
Bibliographic record
Abstract
Introduction: Early life adversity is associated with both psychopathology and cardiometabolic diseases, suggesting shared developmental risk factors and biological mechanisms. We aimed to investigate the associations between early life adversity and psychopathology, cardiometabolic diseases and their comorbidity. Methods: Cross-sectional study with participants from the UK Biobank cohort, composed of 502 543 individuals ranging from 37 to 73 years of age. Samples were collected between 2006 and 2010. Participants were excluded if fitting any of the exclusion criteria-dropouts, inconsistencies in reported versus genotyped sex, inconsistencies in assigned family structure, missing covariates data. We employed generalised estimated equations to evaluate the associations between self-reported adversity and outcomes. Adversity was measured through three scores: prenatal (n=234 817), postnatal (n=151 297) and cumulative (n=82 758). Outcomes were psychopathologies (mental/behaviour disorder due to substance use, mood disorders, schizophrenia, neurotic/stress-related) and cardiometabolic diseases (type 2 diabetes, ischaemic heart disease, atherosclerosis, cerebral infarction, cerebral atherosclerosis). Results: The cumulative combined adversity score predicted comorbidity (OR = 1.38, 95% CI 1.31 to 1.45, p<0.01), as well as all outcomes except cerebral infarction. Similar results were observed analysing prenatal (OR=1.27, 95% CI 1.23 to 1.31, p<0.01) and postnatal (OR=1.43, 95% CI 1.36 to 1.50, p<0.01) adversity scores. Conclusion: Early life adversity may be associated with a common cascade of events linked to both psychopathologic and cardiometabolic diseases, which has profound implications for early prevention, diagnosis and treatment.
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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".