Clinical research in pediatric nephrology: state of the art

Current drug development challenges in chronic kidney disease (CKD)--identification of individualized determinants of renal progression and premature cardiovascular disease (CVD)

Chronic kidney disease (CKD) and end-stage renal disease (ESRD) are currently considered as major health burdens. Notably, CKD can be regarded as an interesting clinical model of accelerated cardiovascular disease (CVD) and ageing, which offers exciting new perspectives and challenges for pharmaceutical drug development. However, during the last decades, therapeutic advances to slow down the progression of CKD and reduce CVD risk have largely failed due to several possible reasons including (i) the lack of profound understanding of the pathophysiology of chronic renal damage and its associated CVD; (ii) an inadequate characterization of molecular mechanisms of currently approved therapies such as renin-angiotensin-aldosterone-system (RAAS) blockade; (iii) the unclear biochemical property needs required for novel therapeutic approaches; (iv) the missing quantity and quality of clinical trials in the nephrology field; and, most importantly, (v) the absence of prognostic renal biomarkers that reflect the severity of the structural organ damage and predict ESRD as well as CVD mortality. There is clearly an insufficient understanding of why a significant proportion of CKD patients progress to ESRD and/or die from CVD whereas others rather remain stable. In this article, we urge renal researchers to develop novel experimental and clinical tools for rational and translational drug discovery. Identification of individualized determinants of CKD progression and/or premature CVD will enable personalized medicine and lead to novel innovative nephro- and/or cardioprotective pharmacological treatment in these high-risk patients.

The role of causal criteria in causal inferences: Bradford Hill's "aspects of association"

As noted by Wesley Salmon and many others, causal concepts are ubiquitous in every branch of theoretical science, in the practical disciplines and in everyday life. In the theoretical and practical sciences especially, people often base claims about causal relations on applications of statistical methods to data. However, the source and type of data place important constraints on the choice of statistical methods as well as on the warrant attributed to the causal claims based on the use of such methods. For example, much of the data used by people interested in making causal claims come from non-experimental, observational studies in which random allocations to treatment and control groups are not present. Thus, one of the most important problems in the social and health sciences concerns making justified causal inferences using non-experimental, observational data. In this paper, I examine one method of justifying such inferences that is especially widespread in epidemiology and the health sciences generally - the use of causal criteria. I argue that while the use of causal criteria is not appropriate for either deductive or inductive inferences, they do have an important role to play in inferences to the best explanation. As such, causal criteria, exemplified by what Bradford Hill referred to as "aspects of [statistical] associations", have an indispensible part to play in the goal of making justified causal claims.