Genetic mapping of habitual substance use, obesity-related traits, responses to mental and physical stress, and heart rate and blood pressure measurements reveals shared genes that are overrepresented in the neural synapse
Bibliographic record
Abstract
Links between substance use habits, obesity, stress and the related cardiovascular outcomes can be, in part, because of loci with pleiotropic effects. To investigate this hypothesis, we performed genome-wide mapping in 119 multigenerational families from a population in the Saguenay-Lac-St-Jean region with a known founder effect using 58,000 single-nucleotide polymorphisms and 437 microsatellite markers to identify genetic components of the following factors: habitual alcohol, tobacco and coffee use; response to mental and physical stress; obesity-related traits; and heart rate (HR) and blood pressure (BP) measures. Habitual alcohol and/or tobacco users had attenuated HR responses to mental stress compared with non-users, whereas hypertensive individuals had stronger HR and systolic BP responses to mental stress and a higher obesity index than normotensives. Genetic mappings uncovered numerous shared genes among substance use, stress response, obesity and hemodynamic traits, including CAMK4, CNTN4, DLG2, FHIT, GRID2, ITPR2, NOVA1 and PRKCE, forming network of interacting proteins, sharing synaptic function and display higher and patterned expression profiles in brain-related tissues; moreover, pathway analysis of shared genes pointed to long-term potentiation. Subgroup genetic mappings uncovered additional shared synaptic genes, including CAMK4, CNTN5 and DNM3 (hypertension-specific); CNTN4, DNM3, FHIT and ITPR1 (sex-specific), having protein interactions with genes driven from general analysis. In summary, consistent with the observed phenotypic correlations, we found substantial overlap among genomic determinants of these traits in synapse, which supports the notion that the neural synapse may be a shared interface behind substance use, stress, obesity, HR, BP as well as the observed sex- and hypertension-specific genetic differences.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".