Cardiovascular Diseases and Metabolic Medications in the Lebanese Population: A Post Hoc Analysis from a Nationwide Cross-Sectional Study
Notice bibliographique
Résumé
OBJECTIVE: This study assesses the association of metabolic drugs (specifically hypoglycemic and hypolipemic agents) with cardiovascular diseases (CVD) among the Lebanese population and patients' subgroups. METHODS: A nationwide cross-sectional retrospective study was carried out in Lebanon. The survey collected information on sociodemographic characteristics, lifestyles, comorbidities, and medication use. Logistic regression models were employed to analyze the data and determine associations between CVD and metabolic drugs. Stratification analyses were performed based on diabetes and dyslipidemia status. RESULTS: The study found significant associations with CVD among the 2048 participants. Higher scores on the Lebanese Mediterranean Diet Score (LMDS; ORa = 1.06), hypertension (ORa = 1.71), diabetes (ORa = 1.75), dyslipidemia (ORa = 1.89), family history of CVD (ORa = 1.58), and smoking (previous: ORa = 1.63, current: ORa = 2.15) were linked to increased CVD odds. Higher income (intermediate: ORa = 0.64, high: ORa = 0.40) was inversely related to it. A subsequent model that included hypoglycemic and lipid-lowering medications yielded similar results. However, neither hypoglycemic nor lipid-lowering medications demonstrated a significant association with CVD risk. A third regression model was conducted by taking the classes of drugs as an independent variable. Also, the result revealed that all the classes of medication were not associated with the risk of CVD. Stratification by diabetes revealed LMDS and hypertension as risk factors in both groups. Among non-diabetic participants, dyslipidemia (ORa = 2.40), current smoking (ORa = 2.28), and higher income (intermediate: ORa = 0.57, high: ORa = 0.62) were linked to CVD. Among people with diabetes, a family history of CVD (ORa = 2.69) increased the CVD odds, while being an employer (ORa = 0.49) lowered it. Stratification by dyslipidemia showed consistent risk factors, and higher LMDS (ORa = 1.07), diabetes (ORa = 2.14), hypertension (ORa = 1.79), and previous smoking (ORa = 1.95) were linked to CVD without dyslipidemia. Being a female (ORa = 0.52) and having a lower income (ORa = 0.40) were associated with lower CVD odds in those with dyslipidemia. Subgroup analyses showed that medications were not significantly associated with CVD odds among patients with diabetes or hyperlipidemia. CONCLUSIONS: This study's findings highlight the importance of addressing modifiable risk factors and socioeconomic factors to reduce the burden of CVD. Targeted interventions and longitudinal research are necessary to optimize preventive strategies and improve the management of CVD in individuals using hypoglycemic and hypolipemic agents in low- and medium-income countries.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».