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New Predictive Models for Alzheimer's Disease

Swedish scientists at Lund University have developed a more accurate way to predict who will get dementia compared to previous methods. Dementia is a class of neurological diseases which results in memory loss and impaired judgement. Alzheimer’s disease is the most common type of dementia, currently affecting millions of people worldwide and posing a serious threat to older adults. Predicting who will develop dementia is an important step in preventing it, but previous methods are either invasive or expensive. Thus, the development of promising new models can support current research on the treatment of dementia.

Before dementia develops, there is some mild but observable cognitive decline. Mild Cognitive Impairment, or MCI for short, means that a person has some difficulty with memory and judgement beyond the effects of normal aging, but it has no serious effects yet. While MCI can indicate the early signs of dementia, it is not a reliable method of prediction - it is entirely possible that a person with MCI will never develop dementia. They may maintain the same diminished level of cognition so that it does not worsen, or potentially return to normal cognition - in either case, the person avoids developing dementia. MCI may also be misdiagnosed; natural cognitive decline from aging may be misinterpreted as MCI. MCI nevertheless indicates risk for dementia, and becomes a source of anxiety when people do not know if they will actually develop dementia later in life. Other methods can be combined with diagnosis of MCI to be more reliable, but these also have major drawbacks. For example, Alzheimer’s disease can be predicted by analysis of cerebrospinal fluid or a PET scan to find certain proteins, or biomarkers, known to lead to the conditions. Still, PET scans are expensive and generally unavailable, while cerebrospinal fluid extraction involves inserting a needle into the spine, an invasive and uncomfortable procedure. Both methods present the possibility of adverse side effects. Another method involves using blood samples to identify protein biomarkers, but this method has yet to be proven effective. Thus, the Lund University scientists set out to test this blood-based method of prediction, comparing it to the proven method of cerebrospinal fluid analysis as well as a more basic model using age, sex, education, and base cognition.

The tests involved measuring three blood biomarker proteins, alpha-beta proteins, tau proteins, and NfL proteins. Biomarkers are essentially molecules associated with certain conditions and are used to indicate if someone has that condition. The tau and NfL proteins were considered the most accurate predictors of progression towards Alzheimer’s disease, while the alpha-beta proteins were not as reliable. Thus, the developed model does not account for the alpha beta proteins. This blood-based method matched the accuracy and can potentially surpass the reliability of the cerebrospinal fluid test. The blood-based method also provides a better model than using age, sex, education, and base cognition. With such promising results, this new model can potentially assist future clinical trials to identify people who present the most risk for Alzheimer’s disease. Additionally, blood-based tests like this eliminate some of the drawbacks of cerebrospinal fluid analysis and PET scans. Blood-based tests are more accessible, cheaper, and less invasive than their counterparts. With more research, they can provide a powerful tool for the prediction and treatment of dementia.

Works Cited:

Cullen, N.C., Leuzy, A., Palmqvist, S. et al. Individualized prognosis of cognitive decline and dementia in mild cognitive impairment based on plasma biomarker combinations. Nat Aging 1, 114–123 (2021). “Mild Cognitive Impairment (MCI).” Alzheimer's Disease and Dementia, Alzheimer's Association, nt.

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