Proteomic Dementia Risk Assessment in Chronic Kidney Disease

Abstract Background Chronic kidney disease (CKD) has relatively high prevalence and independently increases dementia risk. Currently, there is a lack of high‐performance dementia prediction tools designed for the CKD population in clinical practice. Through proteomics discovery, this study aimed to discover more efficient dementia risk models in CKD patients. Method This prospective cohort study utilized data from the UK Biobank, encompassing baseline assessments conducted between 2006 and 2010, with follow‐up extended until September 1, 2023. We included 1355 CKD patients, who were free of dementia at baseline and had available Olink blood proteomics data (approximately 3000 proteins). Feature selection was executed using a least absolute shrinkage and selection operator (LASSO) regression. Generalized Linear Models were employed to derive a proteomic risk model for dementia risk in CKD patients. The model was trained in a tested set of 949 CKD patients and validated in a set of 406 individuals. Mendelian randomization was employed to investigate the causal link between potential proteins and dementia. Result The proteomic risk model, comprised of 17 proteins, demonstrated superior performance compared to conventional clinical tools for dementia risk assessment, including demographic factors (age, sex, education year, APOE ε4 status) and cognitive assessments. During validation, the proteomic model achieved a receiver operating characteristic (ROC) area under the curve (AUC) value of 0.845, significantly surpassing the 0.543 to 0.627 range observed for conventional clinical models. The proteomic plus conventional assessment models did not obtain higher AUC values (ranging from 0.811 to 0.845) (Figure 1). After stratifying participants into two proteomic risk groups based on the median proteomic predicted risk value, the high‐proteomic risk group exhibited 15.14 times increase in the risk of dementia compared to the low‐ proteomic risk group (HR= 15.14, 95%CI: 6.62 ‐ 34.66) (Figure 2). Mendelian randomization analyses indicated a causal link between several proteins and dementia. Conclusion Based on the UK Biobank database, a proteomic risk model for dementia outperformed conventional clinical risk models in CKD patients. This plasma‐derived proteomic model is supposed to have great generalization potential, attributed to the accessibility of blood samples, thereby offering valuable guidance for dementia prevention in the CKD population.

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Journal
Alzheimer s & Dementia
Published
2024-12-01
DOI
https://doi.org/10.1002/alz.092124
Primary Topic
Dementia and Cognitive Impairment Research
Type
article
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0.00
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article

Proteomic Dementia Risk Assessment in Chronic Kidney Disease

Yuye Ning, Zhibo Wang, Jianping Jia, Bote Zhao
Alzheimer s & Dementia
Dementia and Cognitive Impairment Research
article

Proteomic Dementia Risk Assessment in Chronic Kidney Disease

Yuye Ning, Zhibo Wang, Jianping Jia, Bote Zhao
article en

Abstract

Abstract Background Chronic kidney disease (CKD) has relatively high prevalence and independently increases dementia risk. Currently, there is a lack of high‐performance dementia prediction tools designed for the CKD population in clinical practice. Through proteomics discovery, this study aimed to discover more efficient dementia risk models in CKD patients. Method This prospective cohort study utilized data from the UK Biobank, encompassing baseline assessments conducted between 2006 and 2010, with follow‐up extended until September 1, 2023. We included 1355 CKD patients, who were free of dementia at baseline and had available Olink blood proteomics data (approximately 3000 proteins). Feature selection was executed using a least absolute shrinkage and selection operator (LASSO) regression. Generalized Linear Models were employed to derive a proteomic risk model for dementia risk in CKD patients. The model was trained in a tested set of 949 CKD patients and validated in a set of 406 individuals. Mendelian randomization was employed to investigate the causal link between potential proteins and dementia. Result The proteomic risk model, comprised of 17 proteins, demonstrated superior performance compared to conventional clinical tools for dementia risk assessment, including demographic factors (age, sex, education year, APOE ε4 status) and cognitive assessments. During validation, the proteomic model achieved a receiver operating characteristic (ROC) area under the curve (AUC) value of 0.845, significantly surpassing the 0.543 to 0.627 range observed for conventional clinical models. The proteomic plus conventional assessment models did not obtain higher AUC values (ranging from 0.811 to 0.845) (Figure 1). After stratifying participants into two proteomic risk groups based on the median proteomic predicted risk value, the high‐proteomic risk group exhibited 15.14 times increase in the risk of dementia compared to the low‐ proteomic risk group (HR= 15.14, 95%CI: 6.62 ‐ 34.66) (Figure 2). Mendelian randomization analyses indicated a causal link between several proteins and dementia. Conclusion Based on the UK Biobank database, a proteomic risk model for dementia outperformed conventional clinical risk models in CKD patients. This plasma‐derived proteomic model is supposed to have great generalization potential, attributed to the accessibility of blood samples, thereby offering valuable guidance for dementia prevention in the CKD population.

Alzheimer s & DementiaVol. 20(S2)
Ministry of Education of the People's Republic of China (CN), Capital Medical University (CN), Beijing Geriatric Hospital (CN), National Clinical Research Center for Digestive Diseases (CN), Chinese Institute for Brain Research (CN)
Good health and well-being
Openalex Percentile: Top 65%
Dementia and Cognitive Impairment Research
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