Breno Satler Diniz, Zhiduo Chen, David C. Steffens, Luke Pilling, Yuliya Nikolova, Kevan Clifford, Richard H. Fortinsky, George A. Kuchel, Chia-Ling Kuo
{"title":"Proteogenomic signature of Alzheimer’s disease and related dementia risk in individuals with major depressive disorder","authors":"Breno Satler Diniz, Zhiduo Chen, David C. Steffens, Luke Pilling, Yuliya Nikolova, Kevan Clifford, Richard H. Fortinsky, George A. Kuchel, Chia-Ling Kuo","doi":"10.1038/s44220-025-00460-0","DOIUrl":null,"url":null,"abstract":"The mechanisms linking a history of major depressive disorder (MDD) to an increased risk of Alzheimer’s disease and related dementia (ADRD) are not fully understood. Using the UK Biobank, we evaluated the biological mechanisms linking the conditions. In participants without history of MDD, 493 proteins were significantly associated with the risk of ADRD. By contrast, in participants with a history of MDD at baseline, a smaller set of six proteins were significantly associated with ADRD risk (NfL, GFAP, PSG1, VGF, GET3 and HPGDS), with GET3 being specifically associated with ADRD risk in the latter group. Two-sample Mendelian randomization analysis showed that the apolipoprotein E and IL-10 receptor subunit B genes were causally linked to incident ADRD. Finally, we developed a proteomic risk score (PrRSMDD-ADRD), which showed strong discriminative power (C statistic = 0.84) to identify participants with MDD who developed ADRD on follow-up. Here we show that plasma proteins associated with inflammation and amyloid-β metabolism are causally linked to a higher ADRD risk in individuals with MDD. Moreover, the PrRSMDD-ADRD can be useful to identify individuals with the highest risk of developing ADRD in a highly vulnerable population. This study investigates biological mechanisms connecting major depressive disorder to Alzheimer’s disease risk, identifying six key proteins and establishing a proteomic risk score with strong predictive power for identifying vulnerable individuals at risk of developing dementia.","PeriodicalId":74247,"journal":{"name":"Nature mental health","volume":"3 8","pages":"879-888"},"PeriodicalIF":8.7000,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nature mental health","FirstCategoryId":"1085","ListUrlMain":"https://www.nature.com/articles/s44220-025-00460-0","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
The mechanisms linking a history of major depressive disorder (MDD) to an increased risk of Alzheimer’s disease and related dementia (ADRD) are not fully understood. Using the UK Biobank, we evaluated the biological mechanisms linking the conditions. In participants without history of MDD, 493 proteins were significantly associated with the risk of ADRD. By contrast, in participants with a history of MDD at baseline, a smaller set of six proteins were significantly associated with ADRD risk (NfL, GFAP, PSG1, VGF, GET3 and HPGDS), with GET3 being specifically associated with ADRD risk in the latter group. Two-sample Mendelian randomization analysis showed that the apolipoprotein E and IL-10 receptor subunit B genes were causally linked to incident ADRD. Finally, we developed a proteomic risk score (PrRSMDD-ADRD), which showed strong discriminative power (C statistic = 0.84) to identify participants with MDD who developed ADRD on follow-up. Here we show that plasma proteins associated with inflammation and amyloid-β metabolism are causally linked to a higher ADRD risk in individuals with MDD. Moreover, the PrRSMDD-ADRD can be useful to identify individuals with the highest risk of developing ADRD in a highly vulnerable population. This study investigates biological mechanisms connecting major depressive disorder to Alzheimer’s disease risk, identifying six key proteins and establishing a proteomic risk score with strong predictive power for identifying vulnerable individuals at risk of developing dementia.