The influence of genetics and psychosocial factors on cardiovascular diseases
Bibliographic record
Abstract
Background: Cardiovascular disease (CVD) can be subcategorized into heart-related disorders(HRD) and peripheral/vascular-related disorders (PVRD). Genome-wide association studies (GWAS) have mainly identified genetic variants associated with HRD that can be used to develop a polygenic risk score (PRS) to quantify genetic risk. While GWAS have mainly been conducted in middle-aged adults, previous research suggests that genetics may have less of an influence on the risk of chronic diseases among the elderly and non-genetic factors may play a larger role. This includes psychosocial factors (PSFs) such as depression and social isolation that have been associated with CVD. Although gene-environment interactions studies have reported that a healthy lifestyle may mitigate polygenic risk of CVD, PSFs as moderators of polygenic risk of CVD have not been explored.Methods This cross-sectional study analyzed baseline data (n=9,892) from the Canadian Longitudinal Study on Aging. A PRS for CVD was constructed with 39 single nucleotide polymorphisms. Depressive symptoms assessed by the Center for Epidemiological Studies – Depression Scale were categorized into: “none” (Group 1, reference), “current” (Group 2), “clinical depression with no current symptoms” (Group 3) and “potential, recurrent depression” (Group 4). Social isolation index as a binary variable was comprised of marital status, living arrangements, retirement status, contacts, and social participation. The outcome measures were heart-related disorders (HRD: myocardial infarction, angina and heart disease) and peripheral/vascular-related disorders (PVRD: stroke, peripheral vascular disease and hypertension). Logistic regression was performed to generate adjusted odds ratios (ORs) for individual and interactive associations of PRS and PSFs on CVD, according to middle-aged (45- 69 years) and elderly (≥70 years) subgroups.vResults After adjusting for age, biological sex, total household income, education, smoking status, immigration status, province, urban/rural classification and the first five principal components of ancestry, PRS associated with HRD and PVRD among middle-aged participants (OR (95% confidence interval) (HRD: 1.06 (1.03-1.08) and PVRD: (1.02 (1.00-1.03)) but only with HRD among elderly (1.06 (1.03-1.08)). Among middle-aged participants, compared to the reference (group 1), the higher depressive symptoms groups associated with both HRD and PVRD, respectively (group 3: 1.21 (1.21-2.01), 1.49 (1.28-1.74); group 4: 1.75 (1.28-2.39), 1.73 (1.41-2.12)), while group 2 of depressive symptoms associated with only PVRD (1.28 (1.07- 1.53)). Among elderly participants, only group 4 compared to reference associated with PVRD (1.69 (1.08-2.64)). Social isolation associated with only PVRD among middle-aged participants (1.84 (1.04-3.26)). No significant PRS*PSFs interactions were observed.Conclusion: This study suggests that PSFs may not act as moderators for polygenic risk of CVD. However, genetics and PSFs are individually associated with CVD, which may vary according to the stage of the life course and anatomical location of CVD outcome
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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 | 0.001 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".