Notice bibliographique
Résumé
In the last 2 decades, the number of PubMed citations including the term biomarker or marker has increased exponentially by 8.6% additional citations per year, from 9863 citations in 1992 to 55,190 in 2012. The number of citations in nephrology has increased by 9.7% per year, from 576 in 1992 to 3931 in 2012. These studies examined diverse biologic clues, involving changes in blood, urine, or body tissues of the levels or expression of small molecules, proteins, enzymes, DNA, RNA, and antibodies. The goals varied, but included attempts to gain insight into disease mechanisms, to develop new screening, diagnostic, or treatment strategies, to assess the efficacy of interventions, or to identify cases more likely to respond to treatment or with worse prognosis. This hectic hunt for the next promising biomarker is fueled by the need for more reliable tools to inform clinical decision making and health policy. In fact, in several medical disciplines, including nephrology, disease cases are identified too late to fully benefit from interventions of proven efficacy or referral to specialist clinics. For example, guidelines recommend early recognition of CKD and risk assessment, because timely implementation of some available therapies can slow disease progression and reduce the incidence of cardiovascular complications.1,2 However, people with CKD are commonly referred to nephrologists late, usually when the estimated GFR (eGFR) is <30 ml/min per 1.73 m2.3,4 Case classification improvement resulting from the inclusion of proteinuria in addition to eGFR within the new CKD classification system5 suggests that combinations (panels) of biomarkers (biomarker “signatures”) may be more useful than single-molecule indicators. However, the quest for new biomarkers may take several years, might cost hundreds of millions of dollars, and may never translate into helpful clinical tools.6 In this issue of the JASN, O’Seaghdha et al.7 report data on galectin-3, a soluble β-galactoside-binding lectin highly expressed in monocytes, which plays important regulatory roles in inflammation, immunity, and cancer,8 and may be involved in the pathogenesis of atherosclerosis,9,10 diabetes,11 and asthma.12 Interest in galectin-3 is justified by its profibrotic properties. Galectin-3 has been shown to promote TGF-β–mediated activation of fibroblasts into matrix-secreting myofibroblasts in liver13 and renal tissues.14 In hypertrophied hearts, galectin-3 is upregulated and has a stimulatory effect on macrophage migration, fibroblast proliferation, and development of fibrosis.8 Higher levels of galectin-3 have been linked to reduced eGFR in cross-sectional studies,15 new-onset heart failure in Framingham Offspring participants,16 and mortality in subjects with heart failure.17 Because the liver primarily excretes galectin-3,18 elevations of its levels before overt kidney disease would potentially make it a useful biomarker to identify people at risk for CKD (e.g., those with diabetes or hypertension). O’Seaghdha et al.7 hypothesized that galectin-3 may predict new-onset CKD and progression of CKD in the general population, and studied the association of galectin-3 measured at examination 6 (1995–1998) in 2450 Framingham Offspring participants with follow-up data at examination 8 (2005–2008). Consistent with the study hypothesis, galectin-3 predicted rapid decline in eGFR (≥3 ml/min per 1.73 m2 per year) and new-onset CKD (eGFR <60ml/min per 1.73 m2), but not development of albuminuria (albumin/creatinine ratio ≥17 mg/g for men or ≥25 mg/g for women). This study is important for several reasons. First, it is the first relatively large longitudinal population-based study reporting data on the relationship between galectin-3 measured at baseline in a cohort of people with normal kidney function and distant clinical outcomes, including objective measures of CKD. This temporality criterion is key to identifying exposure-disease relationships that are potentially causal in nature. Participants were assembled using prespecified criteria and follow-up was relatively complete. Robustness of findings in adjusted analyses (including age) and treating galectin-3 as either a continuous or categorical variable supports the observed association and suggests the existence of a biologic gradient. Second, the relationship is biologically plausible. CKD progression is characterized by development of tubulointerstitial fibrosis19 and galectin-3 is a proven profibrotic mediator, including in renal tissues.14 On the other hand, lack of association between galectin-3 and occurrence of albuminuria suggests that this biomarker predicts tubulointerstitial fibrosis but not glomerular injury. Finally, the findings from this epidemiologic study are coherent with those from in vitro and animal models, and analogous to those from studies in patients with diabetes and cardiovascular disease. Although promising, the associations found in this study between levels of galectin-3 and distant renal events need to be confirmed in different populations and settings to be generalizable. The relatively weak associations described (i.e., 50% risk increase per SD of log-galectin-3 concentration and relatively low measures of net reclassification improvement) do not exclude its potential role in a panel of prognostic biomarkers. However, they reduce its appeal as diagnostic (screening) marker considering that very high degrees of association (i.e., odds ratios >80) are necessary if a biomarker-based test is to yield >90% sensitivity and specificity ([0.9/0.1]/[0.1/0.9]=81).20 More importantly, the prognostic ability of galectin-3 needs to be confirmed in prognostic studies including internal derivation and external validation samples and ultimately randomized controlled trials testing the role of the addition of this new test (or a panel including galectin-3) to data currently used for this purpose, including history, physical examination, and assessment of albuminuria and eGFR trajectories. The new marker or panel would then be assessed in a Bayesian way for its incremental knowledge adding properties. Such studies would consider whether results are consistent across different laboratories as well as whether physicians can correctly interpret findings and use them to make treatment decisions.6 Finally, whether galectin-3 is a disease mediator rather simply a marker of disease can be tested with intervention studies looking at treatments with the potential to attenuate the profibrotic effects of galectin-3.21 In summary, galectin-3 may be causally involved in mechanisms of tubulointerstitial fibrosis and CKD progression, and it is easily measurable and independently associated with renal end-points. Although galectin-3 may not be used as a diagnostic biomarker, further studies may show stronger associations with clinical end-points (i.e., greater odds ratios of highest versus lowest percentiles of galectin-3 levels) in people at risk. Validation studies and clinical trials are required to establish whether galectin-3 can be considered a useful prognostic marker or a mediator of kidney fibrosis and progressive CKD, and therefore a target of new therapies to reduce the risk of end stage kidney failure. Disclosure None.
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 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,002 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 ».