Remnant cholesterol, clinical characteristics and mortality in 41.767 patients from LIPIDOGRAM 2004-2015 studies - the factor analysis for mixed data cluster analysis
Notice bibliographique
Résumé
Abstract Introduction Remnant cholesterol (remnant-C) contributes to residual cardiovascular risk and is produced by metabolism of triglyceride rich proteins. Aim To assess association between clinical characteristics of patients with elevated remnant-C in a large cohort of patients under the care of primary care physicians. Methods The LIPIDOGRAM studies were carried out in the primary care in Poland in 2004, 2006 and 2015. Patients (n=47,398) recruited in all 16 administrative regions in Poland and physicians were proportionally distributed to the number of inhabitants in each administrative region. Each patient was asked to fill the questionnaire on risk factors, chronic diseases, treatment and lifestyle. In the present analysis we included patients with body mass index (BMI )>18.5 kg/m2 aged 18-75 years. We set the cut-off point of fasting remnant cholesterol at >30 mg/dL (>0.77 mmol/L) that differentiated subjects at high risk of cardiovascular events. We carried out Factor Analysis for Mixed Data (FAMD) cluster analysis to discern groups of patients with similar clinical profiles. Results 41,767 patients, for which we had all relevant data, were included in the analysis. Follow-up rate was 97.8%. Three FAMD-derived clinical characteristics patterns accounted for 47.8% of the total variance and were retained for further analysis. The first pattern was associated with higher prevalence of metabolic syndrome (MetS), higher waist circumference, higher levels of non-HDL-C and remnant-C levels. The second pattern was distinguished by lipid parameters, particularly higher HDL-C levels, and was not significantly associated with comorbid conditions. The third pattern was linked to male sex, previous myocardial infarction, smoking, age, lower remnant-C levels, and reduced prevalence of obesity. All patterns were associated with 5-year mortality. The hazard ratio (HR) for mortality per 1 standard deviation (SD) increase in the first pattern score was 1.18 (95% CI: 1.16-1.22, p<0.001). Patients with clinical characteristics corresponding to second pattern had more favorable outcome HR (per 1SD score increase) – 0.75, 95%CI (0.73-0.78, p<0.001). Patients in third cluster, similarly to patients in cluster 1 had increased mortality HR (per 1SD score) – 1.10, (95%CI:1.06-1.15, p <0.001). Higher (>0.77 mmol/l / 30 mg/dl) remnant-C cholesterol significantly contributed to classification of patients into first and third clusters. It was positively associated with first pattern (r=0.64, p<0.001), but negatively with the third pattern (r=0.39, p<0.001). Conclusions Elevated Remnant-C was associated with clinical pattern typical for metabolic syndrome. Interestingly, lower remnant-C levels in patients with comorbidities and more advanced age may not automatically be indicative of a better prognosis – this requires further investigation.Figure 1.Figure 2.
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 | 0,005 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,003 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».