The influence of genetics and psychosocial factors on cardiovascular diseases
Notice bibliographique
Résumé
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
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».