Predicting Alzheimer’s decline: New model offers personalized insights for patients

In a recent study published in the journal Neurology, a team of scientists in the Netherlands constructed a clinical model to predict cognitive decline in Alzheimer’s disease patients using baseline predictors such as Mini-Mental State Examination (MMSE) score, apolipoprotein (APOE) ε4 dose, levels of β-amyloid 1–42 in cerebrospinal fluid, and age.

Study: Predicting Cognitive Decline in Amyloid-Positive Patients With Mild Cognitive Impairment or Mild Dementia. GD Arts / Shutterstock

Background

Although Alzheimer’s disease causes progressive neurodegeneration, the rate of cognitive decline varies considerably across patients. Close to 100 million individuals are believed to be in the early stages of Alzheimer’s disease, where they are experiencing mild cognitive impairment or some form of mild dementia. For an individual experiencing mild cognitive impairment, the progression to dementia occurs on average over the next four years.

New treatments for Alzheimer’s disease are attempting to slow the progression of the disease from mild cognitive impairment stages to dementia by targeting amyloid plaques, but whether these treatments are effective remains uncertain. Furthermore, given the heterogeneity observed in the rates at which mild cognitive impairment progresses to dementia in different patients, understanding the impact of these treatment options on the cognitive decline trajectory is complicated.

It is important to design clinical models that can predict the trajectory of cognitive decline in patients with mild cognitive impairments to determine whether various disease intervention strategies will be effective in slowing cognitive decline and personalizing putative interventions.

About the study

In the present study, the researchers conducted a longitudinal analysis of patients belonging to the Amsterdam Dementia Cohort. The inclusion criteria for this cohort consisted of a baseline diagnosis of mild dementia or mild cognitive impairment along with amyloid positivity and one baseline and one follow-up measurement of MMSE.

Additionally, the researchers also performed a diagnostic workup involving the assessment of medical history, magnetic resonance imaging (MRI) scans, lumbar puncture for obtaining cerebrospinal fluid, and physical, neurological, and neuropsychological assessments. Parameters such as body weight, height, diastolic and systolic blood pressures, education levels, depression, and smoking history were also examined.

The clinical criteria for diagnosing dementia, formulated by the National Institute on Aging and Alzheimer’s Association, were used to diagnose dementia related to mild cognitive impairments or Alzheimer’s disease. Additional neuropsychological assessments and medical examinations were conducted during follow-ups.

Amyloid positivity was determined based on the detection of Alzheimer’s disease biomarkers in the cerebrospinal fluid or amyloid positron emission tomography (PET). Data on the phosphorylated threonine (pTau) and β-amyloid 1–42 levels in the cerebrospinal fluid were also used for the analysis.

The MRI biomarker used in the study was volumetric MRI measurements of the whole brain and left and right hippocampus. The MMSE scores were the primary cognitive outcome assessed in the study, and the researchers used a linear mixed model to determine the change in MMSE scores over time. The Rey Auditory Verbal Learning Test, or RAVLT score, was used as an additional outcome measure.

Cerebrospinal biomarkers and MRI information have been used thus far to predict the risk of dementia, but predictive markers or models to determine the rate of progression from mild cognitive impairment to dementia are lacking. Furthermore, from a patient’s perspective, obtaining prognostic information about the trajectory of cognitive decline is essential for planning disease management and treatment.

Results

The results indicated that the model constructed in this study could efficiently use clinical variables such as sex, age, and baseline MMSE scores to predict the cognitive decline, measured as changes in MMSE or RAVLT scores, in amyloid-positive patients with mild dementia or mild cognitive impairment. These predictions were further improved by including volumetric MRI measurements and pTau and β-amyloid 1–42 levels in the cerebrospinal fluid as predictors.

The researchers found that adding more vascular or clinical risk factors as predictive markers complicated the model and did not improve the predictive performance. They believe that pTau PET measurements could potentially improve the model’s predictive ability but were unable to test it due to inadequate data from the cohort for this parameter.

However, combining polygenic risk scores with clinical predictors could help improve the predictive performance and potentially explain the remaining unexplained variation in interindividual MMSE scores.

The researchers believe that while this model can provide patients and their caregivers with information about the potential cognitive decline trajectories, the findings also highlight the difficulties in arriving at a precise prognosis.

Conclusions

Overall, the study showed that a simple statistical model that used clinical variables such as sex, age, baseline MMSE scores, and data from MRIs and cerebrospinal fluid markers could provide a reliable prediction about the trajectory of cognitive decline in amyloid-positive patients with mild dementia or mild cognitive impairment. While these predictions cannot provide precise prognostic timelines, they can help formulate personalized treatment options.