Additional file 1 of Sex-specific associations between sodium and potassium intake and overall and cause-specific mortality: a large prospective U.S. cohort study, systematic review, and updated meta-analysis of cohort studies
Notice bibliographique
Résumé
Additional file 1: Table S1. The Underlying Causes of Death Based on the Ninth and Tenth Revisions of International Classification of Diseases (ICD-9, and ICD-10). Table S2. Criteria for Healthy Eating Index-2015 Score. Table S3. Risk of Overall and Cause-Specific Mortality Associated with Daily Intakes of Sodium and Potassium and the Sodium-Potassium Ratio Among 237,036 Men. Table S4. Risk of Overall and Cause-Specific Mortality Associated with Intakes of Sodium, Potassium and Sodium-Potassium Ratio Among 179,068 Women. Table S5. Risk of Overall and Cause-Specific Mortality Associated with per 500 mg or per 1-SD Sodium Intake Among 237,036 Men and 179,068 Women. Table S6. Risk of Overall and Cause-Specific Mortality Associated with per 500 mg or per 1-SD Potassium Intake Among 237,036 Men and 179,068 Women. Table S7. Risk of Overall and Cause-Specific Mortality Associated with per 1-SD Sodium-Potassium Ratio Among 237,036 Men and 179,068 Women. Table S8. Search Strategy for Systematic Review. Table S9. Studies included in the meta-analysis. Table S10. Study Characteristics in the Meta-Analysis. Table S11. Assessment of Risk of Study Bias Based on the Newcastle-Ottawa Scale. Table S12. Prespecified Subgroup Meta-Analysis Using Random-Effects Models for the Association Between Sodium Intake and Risk of Cardiovascular Disease, with the Comparison of Highest Versus Lowest Categories of Sodium Intake. Fig. S1. Sex Stratified Associations Between Sodium Intake and Overall and Cause-specific Mortality in Minimally Adjusted Cubic Spline Regression Models. Fig. S2. Sex Stratified Associations Between Potassium Intake and Overall and Cause-specific Mortality in Minimally Adjusted Cubic Spline Regression Models. Fig. S3. Sex Stratified Associations Between Intake of Sodium-Potassium Ratio and Overall and Cause-specific Mortality in Minimally Adjusted Cubic Spline Regression Models. Fig. S4. Sex Stratified Associations Between Sodium Intake and Overall and Cause-specific Mortality in Parsimoniously Adjusted Cubic Spline Regression Models. Fig. S5. Sex Stratified Associations Between Potassium Intake and Overall and Cause-specific Mortality in Parsimoniously Adjusted Cubic Spline Regression Models. Fig. S6. Sex Stratified Associations Between Intake of Sodium-Potassium Ratio and Overall and Cause-specific Mortality in Parsimoniously Adjusted Cubic Spline Regression Models. Fig. S7. Sex Stratified Associations Between Sodium Intake and Overall and Cause-specific Mortality in Multivariable-Adjusted Cubic Spline Regression Models, Excluding the Initial 5 Years of Follow-up. Fig. S8. Sex Stratified Associations Between Potassium Intake and Overall and Cause-specific Mortality in Multivariable-Adjusted Cubic Spline Regression Models, Excluding the Initial 5 Years of Follow-up. Fig. S9. Sex Stratified Associations Between Sodium-Potassium Ratio and Overall and Cause-specific Mortality in Multivariable-Adjusted Cubic Spline Regression Models, Excluding the Initial 5 Years of Follow-up. Fig. S10. Sex Stratified Associations Between Sodium Intake and Overall and Cause-specific Mortality in Multivariable-Adjusted Cubic Spline Regression Models, Excluding the Participants Reporting a History of Diabetes at Baseline. Fig. S11. Sex Stratified Associations Between Potassium Intake and Overall and Cause-specific Mortality in Multivariable-Adjusted Cubic Spline Regression Models, Excluding the Participants Reporting a History of Diabetes at Baseline. Fig. S12. Sex Stratified Associations Between Sodium-Potassium Ratio and Overall and Cause-specific Mortality in Multivariable-Adjusted Cubic Spline Regression Models, Excluding the Participants Reporting a History of Diabetes at Baseline. Fig. S13. Sex-Stratified Associations Between Sodium Intake and Overall and Cause-specific Mortality in Multivariable-Adjusted Cubic Spline Regression Models, Excluding Participants Reporting Poor to Fair Health Status or Unknown Health Status. Fig. S14. Sex-Stratified Associations Between Potassium Intake and Overall and Cause-specific Mortality in Multivariable-Adjusted Cubic Spline Regression Models, Excluding Participants Reporting Poor to Fair Health Status or Unknown Health Status. Fig. S15. Sex-Stratified Associations Between Sodium-Potassium Ratio and Overall and Cause-specific Mortality in Multivariable-Adjusted Cubic Spline Regression Models, Excluding Participants Reporting Poor to Fair Health Status or Unknown Health Status. Fig. S16. Sex-Stratified Associations Between Sodium Intake and Overall and Cause-specific Mortality in Multivariable-Adjusted Cubic Spline Regression Models, Additionally Adjusted for Family Income. Fig. S17. Sex Stratified Associations Between Potassium Intake and Overall and Cause-specific Mortality in Multivariable-Adjusted Cubic Spline Regression Models, Additionally Adjusted for Family Income. Fig. S18. Sex Stratified Associations Between Sodium-Potassium Ratio and Overall and Cause-specific Mortality in Multivariable-Adjusted Cubic Spline Regression Models, Additionally Adjusted for Family Income. Fig. S19. Sex-Stratified Associations Between Sodium Intake and Overall and Cause-specific Mortality in Multivariable-Adjusted Cubic Spline Regression Models, Additionally Adjusted for Dietary Potassium Intake. Fig. S20. Flow Chat for Study Selection Strategy in the Meta-Analysis. Fig. S21. Funnel Plot for Assessment of Publication Bias for the Association Between Sodium Intake and Risk of Cardiovascular Disease (Including Death Due to Cardiovascular Disease). Fig. S22. Influence Analysis Using Forest Plot for the Meta-Analysis on Associations Between Sodium Intake and Risk of CVD (Including CVD Mortality).
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 | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,000 |
| 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) | 0,220 | 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 ».