Clinical genetics is increasingly recognized as an important area within nephrology care. Clinicians require awareness of genetic kidney disease to recognize clinical phenotypes, consider use of genomics to aid diagnosis, and inform treatment decisions. Understanding the broad spectrum of clinical phenotypes and principles of genomic sequencing is becoming increasingly required in clinical nephrology, with nephrologists requiring education and support to achieve meaningful patient outcomes. Establishment of effective clinical resources, multi-disciplinary teams and education is important to increase application of genomics in clinical care, for the benefit of patients and their families. Novel applications of genomics in chronic kidney disease include pharmacogenomics and clinical translation of polygenic risk scores. This review explores established and emerging impacts and utility of genomics in kidney disease.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10921391 | PMC |
http://dx.doi.org/10.1093/ckj/sfae043 | DOI Listing |
Clin Nutr
December 2024
Division of Nephrology, Department of Medicine, Taipei Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, and School of Medicine, Tzu Chi University, Hualien, Taiwan. Electronic address:
Background: Trimethylamine N-oxide (TMAO) is a gut microbial metabolite derived from dietary l-carnitine and choline. High plasma TMAO levels are associated with cardiovascular disease and overall mortality, but little is known about the associations of TMAO and related metabolites with the risk of kidney function decline among patients with chronic kidney disease (CKD).
Methods: We prospectively followed 152 nondialysis patients with CKD stages 3-5 and measured plasma TMAO and related metabolites (trimethylamine [TMA], choline, carnitine, and γ-butyrobetaine) via liquid chromatography‒mass spectrometry.
Crit Care
December 2024
Division of Anesthesia, Critical Care, Pain and Emergency Medicine, UR‑UM103 IMAGINE, University of Montpellier, Nimes University Hospital, Nîmes, France.
Background: In septic shock, the classic fluid resuscitation strategy can lead to a potentially harmful positive fluid balance. This multicenter, randomized, single-blind, parallel, controlled pilot study assessed the effectiveness of a restrictive fluid strategy aiming to limit daily volume.
Methods: Patients 18-85 years' old admitted to the ICU department of three French hospitals were eligible for inclusion if they had septic shock and were in the first 24 h of vasopressor infusion.
Cardiovasc Diabetol
December 2024
INSERMU1138-Centre de Recherche Des Cordeliers, Paris Cite University, Sorbonne University, 75006, Paris, France.
Hypertension, cardiovascular disease and kidney failure are associated with persistent hyperglycaemia and the subsequent development of nephropathy in people with diabetes. Diabetic nephropathy is associated with widespread vascular disease affecting both the kidney and the heart from an early stage. However, the risk of diabetic nephropathy in people with type 1 diabetes is strongly genetically determined, as documented in familial transmission studies.
View Article and Find Full Text PDFBMC Urol
December 2024
The Department of Urology, Guangdong Second Provincial General Hospital, Guangzhou, 510317, China.
Background: Here, we aim to develop and validate a viable prognostic nomogram model for predicting a stone-free rate of kidney stones patients based on retrospective cohort analysis.
Methods: This is a retrospective study that obtained a continuous cohort from the databases of two hospitals (General Hospital of Southern Theater Command, and Guangdong Second Provincial General Hospital), including 522 patients with kidney stones who underwent Endoscopic Combined Intrarenal Surgery (ECIRS) from January 2015 to December 2022.The characteristics of the primary cohort between the SF (stone-free) and SR (stone residue) groups were identified using single factor and multivariate logistic regression analyses.
BMC Infect Dis
December 2024
Department of Intensive Care Medicine, Affiliated Hospital of Chengdu University, Chengdu, Sichuan, China.
Background: Predicting mortality in sepsis-related acute kidney injury facilitates early data-driven treatment decisions. Machine learning is predicting mortality in S-AKI in a growing number of studies. Therefore, we conducted this systematic review and meta-analysis to investigate the predictive value of machine learning for mortality in patients with septic acute kidney injury.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!