A proteomics biomarker for dementia prediction?

On this study, researchers used data from the UK Biobank to find biomarkers from plasma proteomics associated with risk of incident dementia. Their findings highlight the role of proteomics profiling in developing tests that could help identify healthy people at high risk of developing dementia.
A proteomics biomarker for dementia prediction?
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Dementia progresses slowly from the asymptomatic stage to a fully expressed clinical syndrome over many years, usually lasting over a decade or more (1,2). By the time patients present with cognitive disorders, it might be too late as the disease develops to the middle or late stages, and the best period of intervention is missed. As no effective therapy is currently available, correctly determining whether a person will progress to dementia in the near future has become a public health priority (3). Nonetheless, it remains a major challenge for clinicians, and it is unknown how to best predict the onset of dementia. A possible turning point has recently emerged with the advancement of blood-based biomarkers, which could serve as a preferable tool to facilitate early risk screening in the preclinical phase among the general population (4–6).

A total of 52,645 non-demented UK Biobank adults were included, with 1,417 incident dementia cases and a follow-up time of 14.1 years. Of 1,463 plasma proteins, glial fibrillary acidic protein (GFAP) (all-cause dementia (ACD): hazard ratio (HR)=1.53, P=1.35×10-91; Alzheimer’s disease (AD): HR=1.65, P=9.86×10-75; vascular dementia (VaD): HR=1.61, P=2.06×10-31), neurofilament light polypeptide (NEFL) (ACD: HR=1.56, P=7.27×10-76; AD: HR=1.52, P=3.25×10-27; VaD: HR=1.56, P=1.92×10-15), growth/differentiation factor 15 (GDF15) (ACD: HR=1.28, P=5.91×10-16; AD: HR=1.19, P=0.038; VaD: HR=1.43, P=4.99×10-8), and LTBP2 (ACD: HR=1.24, P=7.97×10-9; AD: HR=1.26, P=2.05×10-4; VaD: HR=1.35, P=0.010) consistently associated most with the risk of incident dementia (including ACD, AD, and VaD) and ranked high in terms of weight for dementia prediction.

When tested alone, each of these proteins demonstrated modest predictive accuracies (areas under the curve (AUCs)=0.7~0.8). Combining GFAP (or GDF15) with basic demographic data achieved an excellent prediction for ACD (AUC=0.891) and AD (AUC=0.872) (or VaD (AUC=0.912)). The same parsimonious models yielded AUCs of 0.872, 0.847, and 0.895 for over 10-year ACD, AD, and VaD incidence, respectively. Individuals with higher GFAP levels were 2.32, 2.91, and 2.37 times more likely to develop ACD, AD, and VaD. Notably, GFAP and LTBP2, but not NEFL and GDF15, were highly specific for dementia prediction. Moreover, changes in GFAP and NEFL began to occur 15 years before dementia diagnosis, with concentrations rising more steeply in individuals with incident ACD or AD than in those who remained dementia-free.

Leveraging the largest community-based cohort of blood proteomics and dementia to date, our article strongly highlights GFAP as an optimal biomarker for the prediction of dementia, even 15 years before diagnosis, with implications for screening people at high risk for dementia and for early intervention. The important plasma biomarkers we identified provide a new theoretical basis for the transition of blood testing from scientific research to clinical practice. Moreover, the blood markers we unveiled are easily accessible and popularized, which are capable of predicting future dementia risks with high accuracies in both short and long terms. With our team’s discovery, blood tests are expected to assist clinicians in identifying people at high risk of dementia as early as possible, enabling timely intervention and improving patients’ quality of life.

References:

  1. Scheltens, P., et al. Alzheimer's disease. Lancet 397, 1577-1590 (2021).
  2. Swaddiwudhipong, N., et al. Pre-diagnostic cognitive and functional impairment in multiple sporadic neurodegenerative diseases. Alzheimers Dement 19, 1752-1763 (2023).
  3. Shah, H., et al. Research priorities to reduce the global burden of dementia by 2025. Lancet Neurol 15, 1285-1294 (2016).
  4. Teunissen, C.E., et al. Blood-based biomarkers for Alzheimer's disease: towards clinical implementation. Lancet Neurol 21, 66-77 (2022).
  5. Zetterberg, H. Biofluid-based biomarkers for Alzheimer's disease-related pathologies: An update and synthesis of the literature. Alzheimers Dement 18, 1687-1693 (2022).
  6. Hansson, O., et al. The Alzheimer's Association appropriate use recommendations for blood biomarkers in Alzheimer's disease. Alzheimers Dement 18, 2669-2686 (2022).

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