QEEG characteristics associated with malnutrition-inflammation complex syndrome
Issued Date
2023-02-07
Resource Type
eISSN
16625161
Scopus ID
2-s2.0-85148529960
Journal Title
Frontiers in Human Neuroscience
Volume
17
Rights Holder(s)
SCOPUS
Bibliographic Citation
Frontiers in Human Neuroscience Vol.17 (2023)
Suggested Citation
Jatupornpoonsub T., Thimachai P., Supasyndh O., Wongsawat Y. QEEG characteristics associated with malnutrition-inflammation complex syndrome. Frontiers in Human Neuroscience Vol.17 (2023). doi:10.3389/fnhum.2023.944988 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/82169
Title
QEEG characteristics associated with malnutrition-inflammation complex syndrome
Author's Affiliation
Other Contributor(s)
Abstract
End-stage renal disease (ESRD) has been linked to cerebral complications due to the comorbidity of malnutrition and inflammation, which is referred to as malnutrition-inflammation complex syndrome (MICS). The severity of this condition is clinically assessed with the malnutrition-inflammation score (MIS), and a cutoff of five is used to optimally distinguish patients with and without MICS. However, this tool is still invasive and inconvenient, because it combines medical records, physical examination, and laboratory results. These steps require clinicians and limit MIS usage on a regular basis. Cerebral diseases in ESRD patients can be evaluated reliably and conveniently by using quantitative electroencephalogram (QEEG), which possibly reflects the severity of MICS likewise. Given the links between kidney and brain abnormalities, we hypothesized that some QEEG patterns might be associated with the severity of MICS and could be used to distinguish ESRD patients with and without MICS. Hence, we recruited 62 ESRD participants and divided them into two subgroups: ESRD with MICS (17 women (59%), age 60.31 ± 7.79 years, MIS < 5) and ESRD without MICS (20 women (61%), age 62.03 ± 9.29 years, MIS ≥ 5). These participants willingly participated in MIS and QEEG assessments. We found that MICS-related factors may alter QEEG characteristics, including the absolute power of the delta, theta, and beta 1 bands, the relative power of the theta and beta 3 subbands, the coherence of the delta and theta bands, and the amplitude asymmetry of the beta 1 band, in certain brain regions. Although most of these QEEG patterns are significantly correlated with MIS, the delta absolute power, beta 1 amplitude asymmetry, and theta coherence are the optimal inputs for the logistic regression model, which can accurately classify ESRD patients with and without MICS (90.0 ± 5.7% area under the receiver operating characteristic curve). We suggest that these QEEG features can be used not only to evaluate the severity of cerebral disorders in ESRD patients but also to noninvasively monitor MICS in clinical practice.