A team from Fudan University in Shanghai and the University of Warwick in the United Kingdom has introduced a novel approach for early dementia diagnosis by watching how certain blood plasma proteins lose their normal regulation. The findings appear in Nature Aging and point to a practical biomarker strategy that could one day allow clinicians to spot dementia much earlier than current methods. Cited: Nature Aging.
The research drew on data from the UK Biobank, examining blood samples from more than 52,600 participants collected between 2006 and 2010. By March 2023, 1,417 individuals in the cohort had developed dementia, providing a substantial basis for exploring predictive signals before full clinical onset. Cited: Nature Aging.
To probe the molecular pathways that might forecast dementia, investigators tracked how a defined set of circulating protein markers behaved in the blood. Employing machine learning and data mining, the team built a panel of proteins whose combined patterns could forecast future dementia risk. Cited: Nature Aging.
Among the 1463 detected plasma proteins, GFAP, NEFL, GDF15, and LTBP2 showed the strongest associations with mixed Alzheimer-vascular dementia, Alzheimer’s disease, and vascular dementia. The analysis indicated that higher GFAP levels corresponded to a roughly 2.32-fold increase in dementia risk. Cited: Nature Aging.
Tracking GFAP or GDF15 trends alongside demographic factors may offer a route to identifying dementia long before symptoms become pronounced, the researchers noted. In addition, changes in NEFL appeared to begin more than a decade before diagnosis. Cited: Nature Aging.
The study also emphasizes that fewer than a handful of plasma proteins drive most of the predictive signal, suggesting a focused panel could be practical for screening programs. The approach highlights how noninvasive blood tests, paired with advanced analytics, could someday complement imaging and cognitive testing. Cited: Nature Aging.
While promising, the researchers caution that further validation in diverse populations is essential before any clinical adoption. They propose longitudinal studies to confirm timing, specificity, and sensitivity across different dementia subtypes and risk groups. Cited: Nature Aging.
Beyond immediate clinical impact, this work contributes to a broader shift toward protein-based risk profiling in neurodegenerative diseases. It underscores the potential of combining large-scale biobank data with machine learning to reveal early signals that precede symptomatic decline. Cited: Nature Aging.
In summary, the study from Fudan and Warwick demonstrates that careful monitoring of specific plasma proteins—particularly GFAP, NEFL, GDF15, and LTBP2—along with demographic information, may open a window into dementia risk well before conventional diagnoses. This line of research moves toward scalable, noninvasive screening tools that could eventually support earlier interventions for patients in Canada and the United States. Cited: Nature Aging.