There are nearly 50 million people living with dementia globally. Furthermore, ~50-70% of all dementia cases are caused by Alzheimer’s Disease (AD). Dementia is also preceded by mild cognitive impairment (MCI), which can either lead to cognitive decline and dementia (due to AD or other disorders) or can remain benign and stable. Therefore, it is important to obtain an accurate prognosis during the MCI stage.
At this stage, key pathological hallmarks of AD can be detected in vivo. For example, cerebrospinal fluid (CSF) biomarkers can be measured, or positron emission tomography (PET) can be used to identify defining features of AD, such as Aβ and tau. However, these technologies have limited usefulness due to the invasiveness of lumbar punctures and high cost of PET imaging.
Alternatively, blood-based biomarkers of Aβ and tau can be measured. These have already been used to accurately distinguish AD patients from controls and from patients with non-AD neurodegenerative disorders. A recent study, published in Nature, measured these plasma biomarkers and tested which subset best predicted individual risk for cognitive decline and progression to AD dementia. They also compared the prognostic ability of these biomarkers compared to the same biomarkers measured in CSF.
Detecting Dementia
The study investigated the patient-level prognostic value of different plasma AD biomarkers. The mini-mental state examination (MMSE) is a cognitive tool used to test for Alzheimer’s and Dementia. The researchers measured the MMSE scores of patients at baseline and 4 years later to measure cognitive decline. This was then modelled combined with different combinations of biomarkers.
In general, using a plasma-based model was either no worse than or even better than using CSF biomarkers. It was also superior to basic models, such as age, sex, education and baseline cognition. The researchers found that the biomarkers with the greatest predictive power were Plasma P-tau181 combined with NfL. These were most effective at predicting primary outcomes of decline and clinical progression to AD dementia.
Predictive Value
The researchers also developed an online tool which can be used to accurately provide individualised prognosis in MCI. This can be used to predict the MMSE scores of an individual 4 years after their baseline visit and predict their percentage risk of progressing from MCI to AD dementia over the same time interval. This biomarker driven prediction model could be used to improve treatment and care and increase the power of clinical trials by only including those with a high risk of future progression.
Symptoms of AD are believed to be linked to tau pathology. Therefore, it is unsurprising that P-tau181 biomarkers were included in the best performing models. In contrast, biomarkers such as plasma Aβ42/Aβ40 was not included in the best model. However, Aβ pathology causes an increase in the levels of tau biomarkers. Therefore, it is logical that plasma Aβ biomarkers may not provide additional prognostic information in MCI when an efficient plasma P-tau measure is included.
Study Strengths & Limitations
Throughout the study, plasma biomarkers were compared to CSF-based models as a performance benchmark. The results of the studies were consistent when different assays were used to measure the levels of different plasma biomarkers, suggesting their results are reliable.
The researchers also demonstrated that including the APOEε4 genotype in the basic model did not dampen the effect of including plasma biomarkers. This is important as this gene is the most common cause of Alzheimer’s in the elderly and it is highly heritable, thus there are ethical issues regarding disclosing this genetic status to patients.
However, the researchers acknowledge that their sample size was relatively limited, therefore significant differences between models were difficult to establish. Further studies of larger and more diverse populations may result in more precise and generalisable models.
Future Treatments of Dementia
Plasma-based AD biomarkers can provide prognostic information in MCI at an individual level. The accuracy of these models was shown to be comparable to CSF biomarkers. The plasma biomarkers P-tau181 in combination with NfL seem to best predict cognitive decline and clinical progression. Thus, these plasma biomarkers AD may allow the individualized risk assessment for patients with MCI, which represents a critical step towards accessible precision medicine for cognitive diseases.
