Unbiased kidney-centric molecular categorization of chronic kidney disease as a step towards precision medicine.

Current classification of chronic kidney disease (CKD) into stages using indirect systemic measures (estimated glomerular filtration rate (eGFR) and albuminuria) is agnostic to the heterogeneity of underlying molecular processes in the kidney thereby limiting precision medicine approaches. To generate a novel CKD categorization that directly reflects within kidney disease drivers we analyzed publicly available transcriptomic data from kidney biopsy tissue. A Self-Organizing Maps unsupervised artificial neural network machine-learning algorithm was used to stratify a total of 369 patients with CKD and 46 living kidney donors as healthy controls. Unbiased stratification of the discovery cohort resulted in identification of four novel molecular categories of disease termed CKD-Blue, CKD-Gold, CKD-Olive, CKD-Plum that were replicated in independent CKD and diabetic kidney disease datasets and can be further tested on any external data at kidneyclass.org. Each molecular category spanned across CKD stages and histopathological diagnoses and represented transcriptional activation of distinct biological pathways. Disease progression rates were highly significantly different between the molecular categories. CKD-Gold displayed rapid progression, with significant eGFR-adjusted Cox regression hazard ratio of 5.6 [1.01-31.3] for kidney failure and hazard ratio of 4.7 [1.3-16.5] for composite of kidney failure or a 40% or more eGFR decline. Urine proteomics revealed distinct patterns between the molecular categories, and a 25-protein signature was identified to distinguish CKD-Gold from other molecular categories. Thus, patient stratification based on kidney tissue omics offers a gateway to non-invasive biomarker-driven categorization and the potential for future clinical implementation, as a key step towards precision medicine in CKD.

COVID-19 response in a long-term care facility for people with epilepsy.

OBJECTIVE: To assess asymptomatic rates and severity of SARS-CoV-2 infection in people with epilepsy and their healthcare workers in a long-term care facility which had implemented weekly surveillance testing between April 2020 and June 2022. METHODS: Questionnaires focused on objective and subjective COVID-19 symptoms for people with epilepsy residing in and their healthcare workers at the Chalfont Centre for Epilepsy in June 2022. Demographic information, comorbidities, and seizure frequency were gathered from medical records. We also collected responses on objective and subjective COVID-19 symptoms from healthcare workers who participated in a prospective study assessing the reaction to COVID-19 vaccinations (SAFER). RESULTS: Fifty-five out of 89 (62%) residents tested positive at least once on weekly PCR testing for SARS-CoV-2 during the period of interest; 20 of those (37%) were asymptomatic. In comparison, of those 63 healthcare workers who tested positive at least once on weekly testing during the same period, only four (6%) were asymptomatic. Of the 159 healthcare workers who also participated in the SAFER study, 41 tested positive at least once, and seven (17%) were completely asymptomatic during infection with SARS-CoV-2. SIGNIFICANCE: People with epilepsy living in a long-term care facility were more likely to present with asymptomatic SARS-CoV-2 infections than healthcare workers at the same facility. Despite possible bias in the reporting of subjective symptoms due to management-by-proxy, there is no evidence that vulnerable people living in an epilepsy long-term care facility showed reduced resilience towards infections. PLAIN LANGUAGE SUMMARY: People with epilepsy living in care home facilities had a surprisingly high degree of asymptomatic infections with SARS-CoV-2. Very few residents had severe or fatal outcomes. This is in stark contrast to the widely reported bad outcomes for people without epilepsy in other care homes. People with epilepsy reported significantly less symptoms than their healthcare workers. No changes in seizure frequency during or after infection were observed.