Evidence map›Paper›PMID 39400832›Full record

ArticleAdvances in experimental medicine and biology2024

Importance of Serum Albumin in Deep Learning-Based Prediction of Cognitive Function Data in the Aged Using a Basic Blood Test.

Kenji Karako, Takeo Hata, Atsushi Inoue, Katsunori Oyama, Eiichiro Ueda, Katsuya Iijima, Yu Chen, Kaoru Sakatani

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Article in Advances in experimental medicine and biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

8 authors.

Kenji KarakoDepartment of Human and Engineered Environmental Studies, Graduate School of Frontier Sciences, The University of Tokyo, Bunkyo City, Tokyo, Japan.
Takeo HataDepartment of Hospital Quality and Safety Management, Osaka Medical and Pharmaceutical University Hospital, Takatsuki, Japan.
Atsushi InoueGraduate School of Life Science and Systems Engineering, Kyushu Institute of Technology, Kitakyushu, Fukuoka, Japan.
Katsunori OyamaDepartment of Computer Science, College of Engineering, Nihon University, Chiyoda City, Tokyo, Japan.
Eiichiro UedaDepartment of Hospital Quality and Safety Management, Osaka Medical and Pharmaceutical University Hospital, Takatsuki, Japan.
Katsuya IijimaInstitute of Gerontology, The University of Tokyo, Bunkyo, Japan.
Yu ChenDepartment of Human and Engineered Environmental Studies, Graduate School of Frontier Sciences, The University of Tokyo, Bunkyo City, Tokyo, Japan.
Kaoru SakataniDepartment of Human and Engineered Environmental Studies, Graduate School of Frontier Sciences, The University of Tokyo, Bunkyo City, Tokyo, Japan. k.sakatani@edu.k.u-tokyo.ac.jp.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundRecently, a method using deep learning has been developed to estimate the risk of developing dementia. This method uses general blood test data from routine health examinations that reveal lifestyle-related diseases, which can lead to vascular cognitive impairment via arteriosclerosis, as well as systemic metabolic disorders that are unrelated to lifestyle, such as nutritional disorders. In this study, we investigated the differences in the accuracy of estimating the risk of dementia based on the presence or the absence of blood test parameters reflecting nutritional disorders while focusing on the association between malnutrition and the risk of dementia in frail, elderly individuals.

objectivesThe objective of this study was to evaluate the impact of including or excluding serum albumin, which reflects nutritional status, on the accuracy of predicting cognitive function in older adults using blood test data.

methodsWe estimated cognitive function, as measured by the Mini-Mental State Examination (MMSE), using the deep learning model (DLM). The estimation was performed based on general blood test data, including complete blood tests and basic metabolic panels, obtained from a selection of 1287 patients admitted to Osaka Medical and Pharmaceutical University Hospital. The data were divided into two groups: individuals aged 65 and above and those aged below 65. The impact of including or excluding serum albumin on the predictive performance of MMSE was examined within each group.

resultsIn those aged below 65, the mean squared error (MSE) of the DLM was 5.33 without albumin and 4.62 with albumin, showing a -0.71 improvement with albumin. In those aged 65 and above, the MSE of the DLM was 6.38 without albumin and 6.28 with albumin, showing a -0.1 improvement with albumin. DISCUSSION: The present study demonstrated that including serum albumin in the input data resulted in lower estimation errors for MMSE across all applied algorithms in the group aged 65 and above. This is consistent with previously reported studies that have shown the adverse effects of malnutrition on cognitive function in older adults.

conclusionsThis study highlighted the significance of serum albumin, which reflects nutritional status, as an important assessment variable for estimating MMSE from blood test data, particularly in individuals aged 65 and above.

Indexed as

CognitionDeep LearningAgedAged, 80 and overCognitive DysfunctionDementiaFemaleHematologic TestsHumansMaleMalnutritionMental Status and Dementia TestsMiddle AgedNutritional StatusSerum AlbuminSerum Albumin, HumanSerum AlbuminSerum Albumin, HumanAlbuminDeep learningDementiaMMSEPrediction of cognitive function

Identifiers

PMID39400832

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