Interaction of Clostridioides difficile infection with frailty and cognition in the elderly: a narrative review.

PURPOSE: Clostridioides difficile infection (CDI) is the leading cause of antibiotic-related diarrhea and healthcare-associated infections, affecting in particular elderly patients and their global health. This review updates the understanding of this infection, with focus on cognitive impairment and frailty as both risk factors and consequence of CDI, summarizing recent knowledge and potential mechanisms to this interplay. METHODS: A literature search was conducted including terms that would incorporate cognitive and functional impairment, aging, quality of life, morbidity and mortality with CDI, microbiome and the gut-brain axis. RESULTS: Advanced age remains a critical risk for severe disease, recurrence, and mortality in CDI. Observational and quality of life studies show evidence of functional loss in older people after acute CDI. In turn, frailty and cognitive impairment are independent predictors of death following CDI. CDI has long-term impact in the elderly, leading to increased risk of readmissions and mortality even months after the acute event. Immune senescence and the aging microbiota are key in susceptibility to CDI, with factors including inflammation and exposure to luminal microbial products playing a role in the gut-brain axis. CONCLUSIONS: Frailty and poor health status are risk factors for CDI in the elderly. CDI affects quality of life, cognition and functionality, contributing to a decline in patient health over time and leading to early and late mortality. Narrative synthesis of the evidence suggests a framework for viewing the cycle of functional and cognitive decline in the elderly with CDI, impacting the gut-brain and gut-muscle axes.

Alterations in the molecular composition of COVID-19 patient urine, detected using Raman spectroscopic/computational analysis.

We developed and tested a method to detect COVID-19 disease, using urine specimens. The technology is based on Raman spectroscopy and computational analysis. It does not detect SARS-CoV-2 virus or viral components, but rather a urine 'molecular fingerprint', representing systemic metabolic, inflammatory, and immunologic reactions to infection. We analyzed voided urine specimens from 46 symptomatic COVID-19 patients with positive real time-polymerase chain reaction (RT-PCR) tests for infection or household contact with test-positive patients. We compared their urine Raman spectra with urine Raman spectra from healthy individuals (n = 185), peritoneal dialysis patients (n = 20), and patients with active bladder cancer (n = 17), collected between 2016-2018 (i.e., pre-COVID-19). We also compared all urine Raman spectra with urine specimens collected from healthy, fully vaccinated volunteers (n = 19) from July to September 2021. Disease severity (primarily respiratory) ranged among mild (n = 25), moderate (n = 14), and severe (n = 7). Seventy percent of patients sought evaluation within 14 days of onset. One severely affected patient was hospitalized, the remainder being managed with home/ambulatory care. Twenty patients had clinical pathology profiling. Seven of 20 patients had mildly elevated serum creatinine values (>0.9 mg/dl; range 0.9-1.34 mg/dl) and 6/7 of these patients also had estimated glomerular filtration rates (eGFR) <90 mL/min/1.73m2 (range 59-84 mL/min/1.73m2). We could not determine if any of these patients had antecedent clinical pathology abnormalities. Our technology (Raman Chemometric Urinalysis-Rametrix®) had an overall prediction accuracy of 97.6% for detecting complex, multimolecular fingerprints in urine associated with COVID-19 disease. The sensitivity of this model for detecting COVID-19 was 90.9%. The specificity was 98.8%, the positive predictive value was 93.0%, and the negative predictive value was 98.4%. In assessing severity, the method showed to be accurate in identifying symptoms as mild, moderate, or severe (random chance = 33%) based on the urine multimolecular fingerprint. Finally, a fingerprint of 'Long COVID-19' symptoms (defined as lasting longer than 30 days) was located in urine. Our methods were able to locate the presence of this fingerprint with 70.0% sensitivity and 98.7% specificity in leave-one-out cross-validation analysis. Further validation testing will include sampling more patients, examining correlations of disease severity and/or duration, and employing metabolomic analysis (Gas Chromatography-Mass Spectrometry [GC-MS], High Performance Liquid Chromatography [HPLC]) to identify individual components contributing to COVID-19 molecular fingerprints.