394-P: Baseline Risk Markers and Visit-to-Visit Variability in Relation to Kidney Outcomes: A Post Hoc Analysis of the PERL Study
Notice bibliographique
Résumé
Background: Baseline risk variables and visit-to-visit variability (VV) of systolic blood pressure (SBP) , HbA1c, serum creatinine, and uric acid (UA) are potential risk markers of kidney function decline in type 1 diabetes. Methods: Post-hoc analysis of the PERL study (NCT02017171) - a double-blind randomized placebo-controlled clinical trial investigating allopurinol’s effect on iohexol-derived glomerular filtration rate (iGFR) in type 1 diabetes with elevated UA. Primary outcome was iGFR change over three years. Linear regression with backwards selection of baseline clinical variables was performed to identify an optimized model forecasting iGFR change. VV of SBP, HbA1c, serum creatinine, and UA were calculated as standard deviations of the residuals in individual linear regression models (LV) and coefficients of variation (CV) using all available measurements in the run-in period; thereafter assessed by linear regression, with iGFR change as the dependent variable. Adjustment included sex, baseline age, diabetes duration, BMI, HbA1c, SBP, kidney function (iGFR or eGFR, as appropriate) , urine albumin excretion rate (UAER) , smoking, and renin-angiotensin system inhibitor use. Results: 422 participants completed the trial, 4were included in this analysis. In the optimized baseline risk marker model, race other (including but not limited to Hawaiian/Pacific Islander) than White, Black, or Asian, higher HbA1c, UAER, and heart rate, and lower iGFR were associated with faster iGFR decline; Hispanic/Latino ethnicity was not. Assessing VV, higher LV and CV of SBP was associated with faster iGFR decline (adjusted LV β: -0.79, p=0.01; CV β: -0.79, p=0.04) ; VVs of HbA1c, creatinine, and UA were not. Conclusions: We identified several risk markers of faster iGFR decline in a high-risk population of individuals with type 1 diabetes. In addition to previously described associations, higher SBP VV was a risk marker for faster kidney function decline. Disclosure V.Rotbain curovic: None. S.Polsky: Advisory Panel; Medtronic, Other Relationship; diaTribe, Research Support; Dexcom, Inc., Eli Lilly and Company, Leona M. and Harry B. Helmsley Charitable Trust, Medtronic, National Institute of Diabetes and Digestive and Kidney Diseases, Sanofi-Aventis U.S. R.Pop-busui: Advisory Panel; Averitas Pharma, Inc., Boehringer Ingelheim International GmbH, Nevro Corp., Novo Nordisk, Reata Pharmaceuticals, Inc., Regenacy Pharmaceuticals, Inc. R.J.Sigal: Advisory Panel; Novo Nordisk Canada Inc., Research Support; Novo Nordisk Canada Inc. K.R.Tuttle: Advisory Panel; Boehringer Ingelheim International GmbH, Gilead Sciences, Inc., Consultant; AstraZeneca, Eli Lilly and Company, Research Support; Bayer AG, Goldfinch Bio, Inc., Novo Nordisk, Travere. G.E.Umpierrez: Research Support; AstraZeneca, Dexcom, Inc., Novo Nordisk. A.Wallia: Advisory Panel; Eli Lilly and Company, Research Support; Novo Nordisk, UnitedHealth Group. S.Rosas: Advisory Panel; AstraZeneca, Teladoc Health, Other Relationship; Bayer AG, Research Support; AstraZeneca, Bayer AG. P.Rossing: Consultant; Astellas Pharma Inc., AstraZeneca, Bayer AG, Gilead Sciences, Inc., Merck Sharp & Dohme Corp., Novo Nordisk A/S, Sanofi, Speaker's Bureau; Eli Lilly and Company. The perl study group: n/a. N.Roy: None. T.W.Hansen: Stock/Shareholder; Novo Nordisk A/S. L.Caramori: Advisory Panel; Bayer AG, Consultant; AstraZeneca, Boehringer Ingelheim International GmbH, Novo Nordisk, Research Support; Bayer AG, Novartis AG. D.Cherney: Other Relationship; AbbVie Inc., AstraZeneca, Bayer AG, Boehringer Ingelheim International GmbH, Janssen Research & Development, LLC, Lilly, Maze, BMS, CSL-Behring, Merck, Otsuka, Novartis and Novo-Nordisk , Mitsubishi Tanabe Pharma Corporation, Sanofi, Research Support; Boehringer Ingelheim-Lilly, Merck, Janssen, Sanofi, AstraZeneca and Novo-Nordisk. I.De boer: Advisory Panel; AstraZeneca, Bayer AG, Cyclerion Therapeutics, Inc., George Clinical, Goldfinch Bio, Inc., Other Relationship; American Society of Nephrology, Research Support; Dexcom, Inc. I.B.Hirsch: Consultant; Abbott Diabetes, Bigfoot Biomedical, Inc., GWave, Roche Diabetes Care, Research Support; Beta Bionics, Inc., Insulet Corporation, Medtronic. I.Lingvay: Advisory Panel; AstraZeneca, Boehringer Ingelheim International GmbH, Lilly, Mannkind, TARGET PharmaSolutions; Valeritas;; Altimmune; DataRevive; Click; Medscape Duke CRI; Janssen Pharma; Bayer; Intercept, Novo Nordisk, Sanofi, Zealand Pharma A/S, Consultant; Novo Nordisk, Research Support; NovoNordisk; Mylan; Merck, Sanofi. J.B.Mcgill: Advisory Panel; Gilead Sciences, Inc., Lilly Diabetes, MannKind Corporation, Novo Nordisk A/S, Provention Bio, Inc., Salix Pharmaceuticals, Consultant; Bayer AG, Boehringer Ingelheim International GmbH, Research Support; Dexcom, Inc., Novo Nordisk. Funding Supported by grants from the NIDDK (R03-DK-094484, R34-DK-097808, UC4-DK-101108, P30-DK-036836, and P30-DK-020572) , the JDRF (17-2012-377) , the National Center for Advancing Translational Sciences (UL1-TR-002494, UL1-TR- 001422, UL1-TR-002556, UL1-TR-002319, and UL1-TR- 001105) , and the National Institute on Aging (Claude Pepper Center grant number, P30-AG-024824)
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 | 0,014 | 0,010 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,003 | 0,005 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,001 |
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 ».