Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025
Longitudinal plasma proteomics: relation to incident Alzheimer's disease dementia and biomarkers.
СтатьяПоддержка NIH: внешняя программаНазвание и аннотация: Перевод пока не подготовлен
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Lee Eun Hye, Huang Yen-Ning, Park Tamina, Liu Shiwei, Adzibolosu Nicholas, Chaudhuri Soumilee, Chang Changgee, Bice Paula J, Dage Jeffrey L, Brosch Jared R, Gao Sujuan, Apostolova Liana G, Wilcock Donna M, Risacher Shannon L, Saykin Andrew J, Jo Taeho, Nho Kwangsik
Аннотация
INTRODUCTION: We investigated whether longitudinal changes in plasma proteins were associated with baseline cognitive stages related to Alzheimer's disease (AD), their progression, and AD biomarkers.
METHODS: We analyzed longitudinal proteomics (SomaScan 7K) data (N = 347) from the Indiana AD Research Center using linear mixed-effects models for associations with baseline cognitive stages, AD dementia (ADD) conversion, and AD imaging/plasma biomarkers, followed by machine learning analysis to evaluate predictive performance for incident ADD.
RESULTS: Our analysis identified two proteins (ACES and IGFALS) associated with baseline diagnosis stages and six proteins (ACES, C7, ZCD1, IL-17C, CC055, and SO5A1) associated with incident ADD. Longitudinal changes of the identified proteins were also associated with AD imaging/plasma biomarkers. The inclusion of longitudinal protein changes yielded an AUC of 84.8% for predicting incident ADD.
CONCLUSION: Our findings showed molecular signatures for AD progression and the potential of dynamic changes in plasma proteins as biomarkers for predicting incident ADD.
HIGHLIGHTS: Changes in plasma ACES and IGFALS linked to baseline AD cognitive stages Changes in ACES, C7, ZCD1, IL-17C, CC055, and SO5A1 associated with incident ADD Changes in those proteins correlated with baseline AD imaging and plasma biomarkers Proteomics model achieved 84.8% AUC-ROC in predicting incident ADD.
METHODS: We analyzed longitudinal proteomics (SomaScan 7K) data (N = 347) from the Indiana AD Research Center using linear mixed-effects models for associations with baseline cognitive stages, AD dementia (ADD) conversion, and AD imaging/plasma biomarkers, followed by machine learning analysis to evaluate predictive performance for incident ADD.
RESULTS: Our analysis identified two proteins (ACES and IGFALS) associated with baseline diagnosis stages and six proteins (ACES, C7, ZCD1, IL-17C, CC055, and SO5A1) associated with incident ADD. Longitudinal changes of the identified proteins were also associated with AD imaging/plasma biomarkers. The inclusion of longitudinal protein changes yielded an AUC of 84.8% for predicting incident ADD.
CONCLUSION: Our findings showed molecular signatures for AD progression and the potential of dynamic changes in plasma proteins as biomarkers for predicting incident ADD.
HIGHLIGHTS: Changes in plasma ACES and IGFALS linked to baseline AD cognitive stages Changes in ACES, C7, ZCD1, IL-17C, CC055, and SO5A1 associated with incident ADD Changes in those proteins correlated with baseline AD imaging and plasma biomarkers Proteomics model achieved 84.8% AUC-ROC in predicting incident ADD.