Breath Analysis: Identification of Potential Volatile Biomarkers for Non-Invasive Diagnosis of Chronic Kidney Disease (CKD).

Recently, volatile organic compound (VOC) determination in exhaled breath has seen growing interest due to its promising potential in early diagnosis of several pathological conditions, including chronic kidney disease (CKD). Therefore, this study aimed to identify the breath VOC pattern providing an accurate, reproducible and fast CKD diagnosis at early stages of disease. A cross-sectional observational study was carried out, enrolling a total of 30 subjects matched for age and gender. More specifically, the breath samples were collected from (a) 10 patients with end-stage kidney disease (ESKD) before undergoing hemodialysis treatment (DIAL); (b) 10 patients with mild-moderate CKD (G) including 3 patients in stage G2 with mild albuminuria, and 7 patients in stage G3 and (c) 10 healthy controls (CTRL). For each volunteer, an end-tidal exhaled breath sample and an ambient air sample (AA) were collected at the same time on two sorbent tubes by an automated sampling system and analyzed by Thermal Desorption-Gas Chromatography-Mass Spectrometry. A total of 110 VOCs were detected in breath samples but only 42 showed significatively different levels with respect to AA. Nonparametric tests, such as Wilcoxon/Kruskal-Wallis tests, allowed us to identify the most weighting variables able to discriminate between AA, DIAL, G and CTRL breath samples. A promising multivariate data mining approach incorporating only selected variables (showing p-values lower than 0.05), such as nonanal, pentane, acetophenone, pentanone, undecane, butanedione, ethyl hexanol and benzene, was developed and cross-validated, providing a prediction accuracy equal to 87% and 100% in identifying patients with both mild-moderate CKD (G) and ESKD (DIAL), respectively.

Resting state EEG rhythms in different stages of chronic kidney disease with mild cognitive impairment.

Here, we tested that standard eyes-closed resting-state electroencephalographic (rsEEG) rhythms may characterize patients with mild cognitive impairment due to chronic kidney disease at stages 3-4 (CKDMCI-3&4) in relation to CKDMCI patients under hemodialysis (CKDMCI-H) and mild cognitive impairment (MCI) patients with cerebrovascular disease (CVMCI). Clinical and rsEEG data in 22 CKDMCI-3&4, 15 CKDMCI-H, 18 CVMCI, and 30 matched healthy control (HC) participants were available in a national archive. Spectral rsEEG power density was calculated from delta to gamma frequency bands at scalp electrodes. Results showed that (1) all MCI groups over the HC group showed decreased occipital rsEEG alpha power density; (2) compared to the HC and CVMCI groups, the 2 CKDMCI groups had higher rsEEG delta-theta power density; and (3) the CKDMCI-3&4 group showed the lowest parietal rsEEG alpha power density. The present rsEEG measures may be useful to monitor the impact of circulating uremic toxins on brain regulation of cortical arousal for quiet vigilance in CKDMCI patients.

Different Abnormalities of Cortical Neural Synchronization Mechanisms in Patients with Mild Cognitive Impairment due to Alzheimer's and Chronic Kidney Diseases: An EEG Study.

This study tested whether resting state alpha rhythms (8-13 Hz) may characterize mild cognitive impairment due to Alzheimer's disease (ADMCI) compared with MCI due to chronic kidney disease (CKDMCI). Clinical and resting state eyes-closed electroencephalographic (rsEEG) rhythms from 40 ADMCI, 29 CKDMCI, and 45 cognitively normal elderly (Nold) subjects were available in a national archive. Age, gender, and education were matched in the three groups, and Mini-Mental State Evaluation (MMSE) score was paired in the ADMCI and CKDMCI groups. Delta (<4 Hz), theta (4-8 Hz), alpha 1 (8-10.5 Hz), alpha 2 (10.5-13 Hz), beta 1 (13-20 Hz), beta 2 (20-30 Hz), and gamma (30-40 Hz) cortical sources were estimated by eLORETA freeware and classified across individuals by area under the receiver operating characteristic curve (AUROCC). Compared with Nold group, posterior alpha 1 source activities were more reduced in ADMCI than CKDMCI group. In contrast, widespread delta source activities were greater in CKDMCI than ADMCI group. These source activities correlated with the MMSE score and correctly classified between Nold and all MCI individuals (AUROCC = 0.8-0.85) and between ADMCI and CKDMCI subjects (AUROCC = 0.75). These results suggest that early AD affects cortical neural synchronization at alpha frequencies underpinning brain arousal and low vigilance in the quiet wakefulness. In contrast, CKD may principally affect cortical neural synchronization at the delta frequencies. Future prospective cross-validation studies will have to test these candidate rsEEG markers for clinical applications and drug discovery.