title: Do we still need cross-sectional studies in Nephrology? Yes we do!
abstract: Cross-sectional studies represent the second line of evidence (after case reports) in the ladder of evidence aimed at defining disease aetiology. This study design is used to generate hypotheses about the determinants of a given disease but also to investigate the accuracy of diagnostic tests and to assess the burden of a given disease in a population. The intrinsic limitation of cross-sectional studies, when applied to generate aetiological hypotheses, is that both the exposure under investigation and the disease of interest are measured at the same point in time. For this reason, generally the cross-sectional design does not provide definitive proofs about cause-and-effect relationships. An advantage of cross-sectional studies in aetiological and diagnostic research is that they allow researchers to consider many different putative risk factors/diagnostic markers at the same time. For example, in a hypothetical study aimed at generating hypotheses about the risk factors for left ventricular hypertrophy (LVH) in patients with chronic kidney disease, investigators could look at several risk factors as potential determinants of LVH (age, gender, cholesterol, blood pressure, inflammation, etc.) with minimal or no additional costs. In this article, we make examples derived from the nephrology literature to show the usefulness of cross-sectional studies in clinical and epidemiological research.
authors.affiliation: CNR-IFC, Clinical Epidemiology and Physiopathology of Renal Diseases and Hypertension, Reggio Calabria, Italy.
authors.forename: Giovanni
authors.initials: G
authors.lastname: Tripepi
authors.affiliation: CNR-IFC, Clinical Epidemiology and Physiopathology of Renal Diseases and Hypertension, Reggio Calabria, Italy.
authors.forename: Graziella
authors.initials: G
authors.lastname: D'Arrigo
authors.affiliation: ERA-EDTA Registry, Department of Medical Informatics, Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands.
authors.forename: Kitty J
authors.initials: KJ
authors.lastname: Jager
authors.affiliation: ERA-EDTA Registry, Department of Medical Informatics, Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands.
authors.forename: Vianda S
authors.initials: VS
authors.lastname: Stel
authors.affiliation: Department of Clinical Epidemiology, Leiden University Medical Centre, Leiden, The Netherlands.
authors.forename: Friedo W
authors.initials: FW
authors.lastname: Dekker
authors.affiliation: CNR-IFC, Clinical Epidemiology and Physiopathology of Renal Diseases and Hypertension, Reggio Calabria, Italy.
authors.forename: Carmine
authors.initials: C
authors.lastname: Zoccali
mesh_terms: D000367:Age Factors; D001794:Blood Pressure; D002784:Cholesterol / Q000097:blood; D003430:Cross-Sectional Studies; D006801:Humans; D017379:Hypertrophy, Left Ventricular / Q000453:epidemiology; D007249:Inflammation; D009398:Nephrology; D051436:Renal Insufficiency, Chronic / Q000453:epidemiology; D012107:Research Design; D012307:Risk Factors; D012737:Sex Factors
chemical_list: D002784:Cholesterol
keywords: aetiology; chronic kidney disease; confounding; cross-sectional studies; diagnostic research