The effects of nutritional monitoring of the elderly at high risk for dementia using QEEG

258 

Abstract Submission 

Hyunna Kim¹, Ukeob Park², Soyoung Jung³, Haejin Kang³, Yejin Seo⁴, Yoo Kyoung Park^5,6, Seung Wan Kang⁷

¹iMediSync Inc., Seoul, Korea, Republic of, ²iMediSync, Inc., Seoul, seoul, ³Dept. of Medical Nutrition (AgeTech-Service Convergence Major), Kyung Hee University, Yongin, Korea, Republic of, ⁴Dept. of Medical Nutrition(Clinical Nutrition), Kyung Hee University, Yongin, Korea, Republic of, ⁵Dept of Food Innovation and Health, Graduate School of East-West Medical Nutrition, Kyung Hee Univ., Yongin, Korea, Republic of, ⁶Dept. of Medical nutrition, Graduate School of East-West Medical Nutrition, Kyung Hee, Yongin, Korea, Republic of, ⁷iMediSync, Seoul, Seoul

Hyunna Kim  
iMediSync Inc.
Seoul, Korea, Republic of

Ukeob Park  
iMediSync, Inc.
Seoul, seoul

Soyoung Jung  
Dept. of Medical Nutrition (AgeTech-Service Convergence Major), Kyung Hee University
Yongin, Korea, Republic of

Haejin Kang  
Dept. of Medical Nutrition (AgeTech-Service Convergence Major), Kyung Hee University
Yongin, Korea, Republic of

Yejin Seo  
Dept. of Medical Nutrition(Clinical Nutrition), Kyung Hee University
Yongin, Korea, Republic of

Yoo Kyoung Park  
Dept of Food Innovation and Health, Graduate School of East-West Medical Nutrition, Kyung Hee Univ.|Dept. of Medical nutrition, Graduate School of East-West Medical Nutrition, Kyung Hee
Yongin, Korea, Republic of|Yongin, Korea, Republic of

Seung Wan Kang  
iMediSync
Seoul, Seoul

Nutritional management has a strong correlation with dementia. There is several research indicating that nutritional therapy or intervention is effective in delaying cognitive impairment.
Electroencephalography(EEG) is used to detect the electrical activity of the brain, not only for verifying the effects of various treatments and drug interventions but also for early detection and severity prediction of cognitive impairment.
This project focuses on nutritional monitoring for individuals at risk of dementia. With the expectation that improving nutritional deficiencies will contribute to the management of chronic conditions, the project aims to validate the therapeutic effects of addressing nutritional deficiencies on dementia using QEEG.

A total of 112 participants from 5 institutions took part in the 10 week study. After application, two individuals did not participate, and 19 were absent in the post-measurement after the pre-measurement. Also, one participant was excluded due to age beyond the analyzable range, resulting in a total of 22 dropouts. The final dataset used for analysis comprised 90 participants with a mean age of 84 years(±8.64), and it consisted of resting-state EEG data. It was confirmed that there were no significant differences in gender and age across institutions(ANOVA p-value=0.39) This study utilized iMediSync, Inc's normative database ISB-Norm DB. The database comprises EEG data from 1,289 healthy participants(553 males,736 females) aged 4 to 80 years old. By comparing and analyzing the standard EEG database matched for age and gender, a calculated Z-score is used to eliminate variability arising from age and gender differences, allowing for a common and statistically robust analysis. At all 5 institutes, dietary intake was monitored and directions to increase the amount of food consumption was provided. EEG measurements were taken before the customized nutritional monitoring over a period of 10 weeks, and post-nutritional monitoring EEG measurements were conducted after the completion of nutritional monitoring. The resting-state EEG of eyes closed(EC) condition was measured at 19 channels of the international 10-20 system. Spectrum power, power ratio, source cortical activity, and imaginary coherence were calculated.

Comparing the EEG of the group(G1) measured before nutritional monitoring with ISB-Norm DB, characteristic features of dementia were identified in G1. Figure1A illustrates an increase in slow-wave band power, a decrease in α activity, and α peak frequency slowing in G1 compared to the Norm DB. As the slow band increases, β waves increase to maintain homeostasis in healthy controls. In cases of dementia, a reduction in β power leads to a higher Theta/Beta Ratio(TBR). Figure1B indicates a significantly higher TBR in the pre-measured data compared to ISB-Norm DB.
The paired T-test comparing G1 and G2(Figure2A) reveals a significant reduction of relative power δ in the overall brain region. In addition, the band powers that were decreased in G1 significantly increased in G2.
However, upon examining the occipital α peak frequency(Figure2B), no substantial changes were evident in the pre/post comparison. In other words, we did not observe an improvement in the slowing of the α peak frequency.
Nevertheless, Figure2C shows that the network power values in G2 are higher compared to G1, and long-distance networks are identified extending from the occipital lobe. This indicates enhancement of the brain network, accompanied by an increase in power in the α2 and β1 frequency bands.

When conducting a paired T-test between the G1 and G2 groups, significant differences were observed in the low-frequency range. Although normalization of α peak frequency was not identified, a relative and significant increase in the previously reduced α and β power was confirmed. Therefore, it can be observed that the dementia pattern seen in the Norm DB is relatively alleviated through 10 weeks of nutritional monitoring.

Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s) ¹

Aging

EEG ²

Degenerative Disease

Electroencephaolography (EEG)

Other - Nutrition