Shaping the risk for late-life neurodegenerative disease: A systematic review on prenatal risk factors for Alzheimer’s disease-related volumetric brain biomarkers
Bibliographic record
Abstract
Environmental exposures including toxins and nutrition may hamper the developing brain in utero, limiting the brain's reserve capacity and increasing the risk for Alzheimer's disease (AD). The purpose of this systematic review is to summarize all currently available evidence for the association between prenatal exposures and AD-related volumetric brain biomarkers. We systematically searched MEDLINE and Embase for studies in humans reporting on associations between prenatal exposure(s) and AD-related volumetric brain biomarkers, including whole brain volume (WBV), hippocampal volume (HV) and/or temporal lobe volume (TLV) measured with structural magnetic resonance imaging (PROSPERO; CRD42020169317). Risk of bias was assessed using the Newcastle Ottawa Scale. We identified 79 eligible studies (search date: August 30th, 2020; Ntotal=24,784; median age 10.7 years) reporting on WBV (N = 38), HV (N = 63) and/or TLV (N = 5) in exposure categories alcohol (N = 30), smoking (N = 7), illicit drugs (N = 14), mental health problems (N = 7), diet (N = 8), disease, treatment and physiology (N = 10), infections (N = 6) and environmental exposures (N = 3). Overall risk of bias was low. Prenatal exposure to alcohol, opioids, cocaine, nutrient shortage, placental dysfunction and maternal anemia was associated with smaller brain volumes. We conclude that the prenatal environment is important in shaping the risk for late-life neurodegenerative disease.
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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".