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Blood circRNAs may predict Alzheimer’s before symptoms emerge
A blood-based circRNA signature could help identify early Alzheimer’s biology and progression risk, offering a promising new layer beyond amyloid and tau testing.
Study: Blood-based circular RNAs for early diagnosis of Alzheimer’s disease. Image Credit: Andrii Vodolazhskyi / Shutterstock
In a recent study published in the journal Nature Medicine, researchers identified circular ribonucleic acids (circRNAs) in blood with high predictive value for biomarker-confirmed early Alzheimer’s disease (AD) diagnosis. Combining these circRNAs with established markers, such as phosphorylated tau-217 (pTau217), yielded the highest predictive ability. These findings suggest that circRNA investigations could eventually complement blood-based AD biomarker panels to identify people with early AD biology or elevated progression risk. However, the findings need to be validated in larger, diverse prospective clinical cohorts.
AD is the leading cause of dementia. Since pathological alterations in this condition appear before cognitive decline, scientists are developing new strategies to detect AD early and support timely intervention aimed at slowing disease progression. Early identification of the disease before clinical symptoms appear could enable prompt treatment and better clinical planning, and may improve outcomes when paired with effective interventions, while potentially reducing mortality associated with severe disease.
About the study
In the present study, researchers analyzed blood samples of 1,221 participants, including 405 AD patients and 816 cognitively unimpaired adults, using RNA sequencing (RNA-seq). They aimed to identify and validate blood-based circRNAs that could help diagnose AD and monitor disease progression. They used the CircAtlas 3.0 database to examine circRNA expression across 33 tissues and quantitative polymerase chain reaction (qPCR) to assess selected circRNA expression in these tissues.
The team calculated area under the curve (AUC) values to determine the diagnostic utility of a model based on the blood-based circRNAs. They compared the results with blood pTau217 levels to classify biomarker-confirmed AD status. The researchers also replicated the results among 551 participants in the Knight Alzheimer's Disease Research Center (Knight ADRC), including 76 with AD and 475 cognitively unimpaired individuals. They additionally tested the model in the preclinical Anti-Amyloid Treatment in Asymptomatic AD cohort (A4, 1,767 participants), in which almost all participants were cognitively unimpaired at baseline. They used logistic regression models, including the top differentially expressed circRNAs, for statistical analysis.
Among the Knight ADRC participants, the team evaluated the ability of circRNAs and pTau217 biomarkers in blood, and of amyloid-PET status, to predict symptomatic progression. They used Cox regression models to estimate the hazard ratios (HRs) for this analysis.
The team assessed the specificity of blood-based circRNAs for disease detection by comparing findings across other neurodegenerative conditions, including Parkinson’s disease (PD), dementia with Lewy bodies (DLB), and frontotemporal dementia (FTD). They also evaluated whether the overall 34-circRNA model could predict progression of dementia severity using Clinical Dementia Rating (CDR) scores. They also conducted sensitivity analyses stratified by sex, ancestry, and apolipoprotein E4 (APOE4) status. They performed principal component analysis (PCA) to generate covariates for genetic ancestry.
Results
The team identified 34 circRNAs linked to clinical AD status. The overall 34-circRNA prediction signal linearly and consistently increased from the presymptomatic stage around two to four years before symptom onset until symptomatic AD. Most of the identified AD-related circRNAs were highly expressed and showed preferential expression in the brain, although the study could not prove that the blood circRNAs were brain-derived, and their links with clinical AD were observed regardless of their cognate linear messenger RNA counterparts. The overall circRNA model scores were associated with dementia severity and could capture dynamic signals of AD progression that other pathology-focused biomarkers might miss.
The results were comparable to blood pTau217 levels and also replicated in the A4 and Knight ADRC study groups. The circRNA-based model outperformed blood pTau217 alone for biomarker-confirmed A−T− cognitively unimpaired versus A+T+ AD classification, achieving an AUC of 0.95 compared with 0.88 for blood pTau217 alone. The team achieved the highest AUC by integrating both biomarkers (0.97-0.98). The combined circRNA and pTau217 model helped differentiate non-progressors from high-risk progressors. This could be potentially useful for monitoring AD progression in the era of new AD treatments, especially those targeting amyloid plaques, as circRNAs may indicate broader biological changes and symptom progression beyond amyloid pathology.
The blood-based circRNA model also specifically detected AD-related changes and showed low predictive performance for conditions such as PD, DLB, and FTD. These markers may therefore potentially help stratify progression risk and be explored for monitoring disease biology beyond amyloid pathology. Among Knight ADRC participants, circRNAs (HR, 2.9) outperformed pTau217 (HR, 1.8) and amyloid-PET in predicting progression to the symptomatic stage of AD. The sensitivity analysis yielded similar results, highlighting the robustness of the primary findings. The findings were largely similar for European, African, and mixed populations, supporting potential robustness across ancestries, although some ancestry subgroups were small.
Conclusion
The findings highlight blood-based circRNAs as promising, non-invasive, scalable, and high-precision investigational biomarkers for predicting biomarker-confirmed AD status and symptomatic progression risk. Based on these findings, circRNA detection in blood could one day be used as an adjunct to early AD detection, provided the findings are validated in larger, prospective clinical studies. In the future, researchers should also explore the influence of AD-related comorbidities on blood-based circRNA levels.
The findings are especially relevant since circRNAs are highly stable, tissue-specific, and can be measured in blood. This approach may be clinically useful because traditional AD biomarker assessment has often relied on cerebrospinal fluid (CSF) obtained through lumbar puncture or expensive amyloid PET scans.
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