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Enregistrement W2968581900 · doi:10.2215/cjn.00370119

The Association of Fenofibrate with Kidney Tubular Injury in a Subgroup of Participants in the ACCORD Trial

2019· letter· en· W2968581900 sur OpenAlexafffundabout
Kinsuk Chauhan, Girish N. Nadkarni, Neha Debnath, Lili Chan, Aparna Saha, Amit X. Garg, Chirag R. Parikh, Steven G. Coca

Notice bibliographique

RevueClinical Journal of the American Society of Nephrology · 2019
Typeletter
Langueen
DomaineMedicine
ThématiqueDiabetes Treatment and Management
Établissements canadiensInstitute for Clinical Evaluative SciencesWestern University
Organismes subventionnairesNational Center for Advancing Translational SciencesNational Institute of Diabetes and Digestive and Kidney DiseasesNational Heart, Lung, and Blood InstituteCanadian Institutes of Health ResearchNational Institutes of Health
Mots-clésMedicineFibrateFenofibrateCreatinineInternal medicineRenal functionDiabetes mellitusPlaceboRandomized controlled trialAcute kidney injuryType 2 diabetesKidney diseaseDiscontinuationStatinEndocrinology

Résumé

récupéré en direct d'OpenAlex

Diabetes is a leading cause of ESKD worldwide, resulting in substantial patient morbidity and healthcare costs. Fenofibrate and other fibrate therapies may improve lipid profiles and prevent cardiovascular events in people with CKD, and thus may be useful as adjunctive therapies to reduce the risk of cardiovascular disease (1). However, fibrates have been linked to an increase in the concentration of serum creatinine, the cause of which is uncertain and may be reversible upon discontinuation of the fibrate. Recent published data from the Action to Control Cardiovascular Risk in Diabetes (ACCORD) clinical trial confirms a higher risk of doubling the concentration of serum creatinine from baseline with fenofibrate versus placebo (2). These participants were a part of the overall ACCORD trial, which was a two-by-two factorial design trial with type 2 diabetes, who were being treated with open-label simvastatin to receive either masked fenofibrate or placebo. In this study we measured new urinary biomarkers in ACCORD participants to establish that the rise is serum creatinine with fibrate use is not associated with markers of inflammation or kidney injury (3,4). These biomarkers have been shown to help dissociate increases in serum creatinine due to hemodynamic or noninjury causes versus actual tubular injury in previous studies (3,4). We obtained urine samples (at baseline and at 2 years) from a subgroup of 571 ACCORD participants, not randomly selected (a mix of matched CKD 1:1 case-control and randomized patients) who were all receiving statins. These patients were randomized to either placebo (n=329) or fenofibrate (n=242) at the initiation of the ACCORD study. We measured four urine biomarkers: kidney injury molecule-1 (KIM-1) and IL-18, two biomarkers specific to injury or damage to kidney tubules; monocyte chemoattractant protein (MCP-1), a biomarker of inflammation; and a biomarker of repair (YKL40) of kidney tubules. We measured these biomarkers once per sample using the four-plex prototype assay on the Mesoscale Platform (Meso Scale Diagnostics). Intra- and interassay coefficients of variation were 2.2%–6.8% for urinary IL-18, 5.0%–9.3% for urinary KIM-1, 3.6%–15.3% for urinary MCP-1, and 1.6%–12.1% for urinary YKL-40. The average lower limit of detection obtained from multiple runs was 0.09 pg/ml for IL-18, 0.28 pg/ml for KIM-1, 0.05 pg/ml for MCP-1, and 0.16 pg/ml for YKL-40. Our primary outcome was the change in the concentration of these urinary biomarkers and the changes in eGFR and random urine albumin-to-creatinine ratio (UACR) over 2 years. Median (interquartile range) were calculated for baseline and follow-up period comparing both arms. We used quantile regression method to estimate the median change and 95% confidence interval from baseline to year 2 for eGFR, serum creatinine, and urinary biomarkers. We also calculated the ratio of the mean change at year 2 for fenofibrate arm versus placebo arm. The median (25th, 75th percentile) age was 62 (57, 66) years, eGFR was 89 (76–95) ml/min per 1.73 m2, and UACR was 13 (6–48) mg/g; 58% were men and 74% were white. Baseline clinical characteristics including BP, medications, comorbidities, eGFR, UACR, and all four biomarker levels were similar in the two randomization groups. At 24 months, eGFR was 22.8 ml/min per 1.73 m2 lower (P<0.001) and serum creatinine 0.30 mg/dl higher (P<0.001) with fenofibrate versus placebo. In contrast, UACR and all four urine biomarkers of kidney injury or repair were similar or decreased from baseline in ACCORD participants randomized to fenofibrate versus placebo (Table 1). In a stratified analysis, even participants with the highest quintile of 2-year decrements in eGFR with fenofibrate (−30.4 to −73.5 ml/min per 1.73 m2) still had lower or unchanged UACR and urine biomarkers over the same 2-year period (data not shown). Table 1. - Effects of fenofibrate versus placebo on longitudinal changes in eGFR, serum creatinine, UACR, and urine tubular damage markers in a subset of participants in ACCORD Kidney-Based Parameters Statin+Fenofibrate P Value Statin+Placebo P Value n=242 n=329 eGFR ml/min per 1.73 m2, median (IQR) Baseline 89.1 (76.2–96.2) <0.001 88.0 (74.4–94.8) 0.02 Year 2 66.3 (51.8–86.2) 84.4 (70.7–93.8) Change from baseline to year 2 (95% CI) −22.6 (−28.1 to −17.1) <0.001 −3.6 (−6.6 to −0.6) 0.02 Ratio of the mean change fibrates versus placebo a ↓18% (↓22%, ↓14%) UACR (mg/g), median (IQR) Baseline 13.0 (6.6–70.9) 0.1 12.3 (6.3–36.8) 0.7 Year 2 9.6 (5.4–29.1) 11.9 (6.4–41.5) Change from baseline to year 2 (95% CI) −3.2 (−8.6 to 2.1) 0.2 −0.4 (−3.8 to 3.0) 0.8 Ratio of the mean change fibrates versus placebo a ↓20% (↓38%, ↑3%) Serum creatinine (mg/dl), median (IQR) Baseline 0.8 (0.7–1.0) <0.001 0.9 (0.8–1.0) 0.4 Year 2 1.1 (0.9–1.3) 0.9 (0.7–1.0) Change from baseline to year 2 (95% CI) 0.3 (0.2 to 0.3) <0.001 −0.04 (−0.1 to −0.00) 0.1 Ratio of the mean change fibrates versus placebo a ↑22% (↑19%, ↑27%) uKIM-1, pg/ml, median (IQR) Baseline 931.0 (488.4–1668.6) 0.002 767.8 (381.4–1492.1) 0.3 Year 2 691.3 (394.4–1178.7) 717.7 (384.3–1323.8) Change from baseline to year 2 (95% CI) −239.7 (−403.3 to −76.1) 0.004 −50.2 (−191.6 to 91.3) 0.5 Ratio of the mean change fibrates versus placebo a ↓6% (↓17%, ↑6%) uIL-18, ng/ml, median (IQR) Baseline 26.7 (13.1–57.4) 0.2 28.0 (13.2–50.9) 0.3 Year 2 24.0 (10.6–41.7) 30.0 (14.4–51.2) Change from baseline to year 2 (95% CI) −2.7 (−8.2 to 2.8) 0.3 2.0 (−3.5 to 7.5) 0.5 Ratio of the mean change fibrates versus placebo a ↓25% (↓34%, ↓14%) uMCP-1, pg/ml, median (IQR) Baseline 164.0 (79.3–293.9) 0.1 158.9 (70.1–286.6) 0.6 Year 2 138.8 (73.1–241.8) 150.2 (71.6–271.3) Change from baseline to year 2 (95% CI) −25.2 (−58.0 to 7.6) 0.1 −8.7 (−37.8 to 20.4) 0.6 Ratio of the mean change fibrates versus placebo a ↓2% (↓14%, ↑11%) uYKL-40, ng/ml, median (IQR) Baseline 319.0 (104.2–675.5) 0.2 273.5 (68.2–734.0) 0.2 Year 2 404.1 (132.4–864.4) 341.2 (107.0–823.2) Change from baseline to year 2 (95% CI) 85.2 (−26.3 to 196.6) 0.1 64.9 (−32.3 to 162.2) 0.2 Ratio of the mean change fibrates versus placebo a ↑8% (↓17%, ↑39%) UACR, urine albumin-to-creatinine ratio; ACCORD, Action to Control Cardiovascular Risk in Diabetes trial; IQR, interquartile range; 95% CI, 95% confidence interval; uKIM-1, urinary kidney injury molecule-1; uIL-18, urinary IL-18; uMCP-1; urinary monocyte chemoattractant protein 1; uYKL-40, urinary human cartilage glycoprotein-39; (↑), increased biomarker levels; (↓) decreased biomarker levels.aRatio of the mean change at year 2 for fenofibrate arm versus placebo arm. In conclusion, initiating fenofibrate increased the concentration of serum creatinine with no concomitant increase in the concentration of a panel of urinary biomarkers representing tubular injury, inflammation, or fibrosis. These findings are consistent with previous findings from ACCORD that fenofibrate discontinuation led the reduction in the concentration of serum creatinine (5). We acknowledge there are some limitations to our methods. These include the following: (1) lack of availability of multiple measurements of serum creatinine; (2) inability to rule out any episodes of AKI; (3) the lack of potential generalizability of the findings to nontrial populations; (4) the coefficient of variation of the biomarker assays may have diminished the ability to detect the true effect (or lack thereof) of fibrates versus placebo over time, with confidence intervals that crossed unity; and (5) because our study population is not entirely randomly selected, there could be an element of potential selection bias. However, despite this, only the upper bound of the ratio of the mean in urine YKL-40 (39%) exceeded the median magnitude of rise in serum creatinine (22%). Moreover, other data demonstrated that the withdrawal of fenofibrate led to a reduction of serum creatinine in the ACCORD trial population also supports the notion the change in serum creatinine on fibrates are likely due to physiologic changes rather than actual kidney tubular injury (5). Given results from this report and other kidney injury biomarker analyses from ACCORD-BP and Systolic BP Intervention Trial (SPRINT) (3,4), more efforts should be made in future clinical trials to measure markers of kidney injury in a portion of trial participants to distinguish benign changes in serum creatinine from true kidney injury. This distinction is clinically important and may allay some prescribers avoiding the use of beneficial medications simply because they cause a nonpathologic rise in the concentration of serum creatinine. Disclosures Dr. Coca, Dr, Nadkarni, and Dr. Parikh are members of the advisory board of RenalytixAI and own equity in the company. Outside of the submitted work, Dr. Coca reports personal fees from CHF Solutions, Goldfinch Bio, Janssen, Takeda, and Quark. Also outside of the submitted work, Dr. Coca reports personal and other fees and stock options from pulseData and personal and other fees from RenalytixAI. Dr. Nadkarni reports personal and other fees from BioVie Inc. and pulseData. Dr. Nadkarni also reports a position as cofounder of and personal and other fees from RenalytixAI. Dr. Parikh reports consulting fees from Akebia Therapeutics, Inc. and Genfit Biopharmaceutical Company and other fees from RenalytixAI. Dr. Chan, Dr. Chauhan, Dr. Debnath, Dr. Garg, and Dr. Saha have nothing to disclose. Funding Dr. Chan is supported by grants from National Institutes of Health and Renal Research Institute. Dr. Coca is supported by a grant from the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), grant R01DK096549. Dr. Garg received partnership funding from Astellas for a research grant funded by the Canadian Institutes of Health Research. Dr. Nadkarni reports a grant from Goldfinch Bio. Dr. Parikh reports grants from NIDDK and the National Heart, Lung, and Blood Institute. Dr. Coca, Dr. Nadkarni, and Dr. Parikh are members and are supported in part by the CKD Biomarker Consortium (1U01DK106962-01).

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,004
score de la tête « metaresearch » (Gemma)0,004
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Commentaire · Signal consensuel: aucune
Score de désaccord entre enseignants0,004
Score d'incertitude au seuil0,022

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0040,004
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0020,002
Bibliométrie0,0000,000
Études des sciences et des technologies0,0010,000
Communication savante0,0010,001
Science ouverte0,0010,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

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.

Tête enseignante Opus0,039
Tête enseignante GPT0,346
Écart entre enseignants0,307 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreCommentaire

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations8
Publié2019
Routes d'admission3
Résumé présentoui

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