Performances comparées du HDL-cholestérol et du ratio cholestérol total/HDL pour le dépistage du syndrome métabolique chez des adultes du Sud-Bénin (Afrique de l’Ouest)
Bibliographic record
Abstract
Les critères de définition du Syndrome métabolique (SMet) n’identifient pas convenablement les sujets d’origine africaine à risque. L’objectif était de déterminer lequel du ratio cholestérol total/HDL-cholestérol (CT/HDL-C) et du HDL-Cholestérol est le meilleur prédicteur du SMet chez les adultes béninois. L’étude de type transversal, issue d’une enquête longitudinale sur le risque cardiométabolique a inclu 416 sujets âgés de 29 à 69 ans. Les composantes du SMet considérées sont : l’obésité abdominale, la tension artérielle élevée, la glycémie élevée, le HDL-C bas et les triglycérides élevés. La comparaison des aires sous les courbes (AUC) de la « fonction d’efficacité du récepteur » (ROC) de prédiction de l’existence deux composantes du SMet autre que l’obésité abdominale, a permis de déterminer le meilleur prédicteur. Les prévalences du SMet étaient de 13,9% selon la définition harmonisée, 12,3% lorsque le HDL-C bas est remplacée par CT/HDL-C élevé. Les prévalences du HDL-bas et du CT/HDL-C élevé sont de 37,7% et 22,6%, respectivement. Pour le dépistage du SMet, l’AUC du CT/HDL-C est de 0,69 (IC 95% 0,61-0,77) chez les femmes et 0,68 (IC 95% 0,59-0,77) chez les hommes. L’AUC du HDL-C est de 0,45 (IC 95% 0,37-0,53) chez les femmes et 0,40 (IC 95% 0,30-0,44) chez les hommes. Le HDL-C et le CT/HDL-C ont une faible capacité prédictive pour le SMet, mais la composante CT/HDL-C prédit mieux le SMet que le HDL-C isolé. Toutefois, l’utilisation de l’un ou l’autre des deux paramètres ne modifie pas substantiellement la prévalence du SMet dans la population d’étude.© 2016 International Formulae Group. All rights reserved.Mots clés: Syndrome métabolique, lipoprotéines, ratio CT/HDL-C, Sud-BéninEnglish Title: Comparative performance of HDL-cholesterol and total cholesterol / HDL ratio for screening of metabolic syndrome in Southern Benin adults (West Africa)English AbstractCurrent definition criteria of the metabolic syndrome (MetS) do not adequately identify at risk African origin subjects. The objective was to determine which of total cholesterol/HDL-cholesterol (TC/HDL-C) and HDL-cholesterol is the best predictor of metabolic syndrome (SMet) in Benin adults. This cross-sectional study was nested in a four-year follow-up study on cardiometabolic risk factors and included 416 adults aged 29-69 years. Components of MetS considered were abdominal obesity, high blood pressure (BP), high fasting glucose, low HDL-C and high triglycerides. Areas under the "Receiver operator characteristic" curves (AUC)for CT/HDL-C and HDL-C in predicting the presence of at least two other components of SMet were compared in order to determine the best predictor of SMet. The prevalence of SMet was 13.9%, when replacing low HDL-C by high TC/HDL-C and 15.3% when both dyslipidemia indicators are combined. The prevalence of low HDL-C and high TC/HDL-C was 37.7% and 22.6%, respectively (p<0.001). Screening for SMet, the AUC of TC/HDL-C were 0.69 (95% CI 0.61-0.77) for women and 0.68 (95% CI 0.59-0.77) in men. The AUC of HDL-C were 0.45 (95% CI 0.37-0.53) for women and 0.40 (95% CI 0.30-0.44) for men. Both TC/HDL-C and HDL-C showed some weak predictive values for SMet, but TC/HDL-C ratio predicted SMet better than HDL-C.© 2016 International Formulae Group. All rights reserved.Keywords: Metabolic syndrome, lipoprotein, ratio CT/HDL-C, Southern Benin
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".