Researchers developed a model to predict cognitive decline in Alzheimer's patients using clinical and biomarker data, improving personalized treatment plans.
By Dr. Chinta SidharthanReviewed by Susha Cheriyedath, M.Sc.Jul 16 2024 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 score, apolipoprotein ε4 dose, levels of β-amyloid 1–42 in cerebrospinal fluid, and age.
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.
Additionally, the researchers also performed a diagnostic workup involving the assessment of medical history, magnetic resonance imaging 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.
Related StoriesThe 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.
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.
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