Machine learning models for dementia screening to classify brain amyloid positivity on positron emission tomography using blood markers and demographic characteristics: a retrospective observational study

Abstract

Background: Intracerebral amyloid β (Aβ) accumulation is considered the initial observable event in the pathological process of Alzheimer's disease (AD). Efficient screening for amyloid pathology is critical for identifying patients for early treatment. This study developed machine learning models to classify positron emission tomography (PET) Aβ-positivity in participants with preclinical and prodromal AD using data accessible to primary care physicians.

Methods: This retrospective observational study assessed the classification performance of combinations of demographic characteristics, routine blood test results, and cognitive test scores to classify PET Aβ-positivity using machine learning. Participants with mild cognitive impairment (MCI) or normal cognitive function who visited Oita University Hospital or had participated in the USUKI study and met the study eligibility criteria were included. The primary endpoint was assessment of the classification performance of the presence or absence of intracerebral Aβ accumulation using five machine learning models (i.e., five combinations of variables), each constructed with three classification algorithms, resulting in a total of 15 patterns. L2-regularized logistic regression, and kernel Support Vector Machine (SVM) and Elastic Net algorithms were used to construct the classification models using 34 pre-selected variables (12 demographic characteristics, 11 blood test results, 11 cognitive test results).

Results: Data from 262 records (260 unique participants) were analyzed. The mean (standard deviation [SD]) participant age was 73.8 (7.8) years. Using L2-regularized logistic regression, the mean receiver operating characteristic (ROC) area under the curve (AUC) (SD) in Model 0 (basic demographic characteristics) was 0.67 (0.01). Classification performance was similar in Model 1 (basic demographic characteristics and Mini Mental State Examination [MMSE] subscores) and Model 2 (demographic characteristics and blood test results) with a cross-validated mean ROC AUC (SD) of 0.70 (0.01) for both. Model 3 (demographic characteristics, blood test results, MMSE subscores) and Model 4 (Model 3 and ApoE4 phenotype) showed improved performance with a mean ROC AUC (SD) of 0.73 (0.01) and 0.76 (0.01), respectively. In models using blood test results, thyroid-stimulating hormone and mean corpuscular volume tended to be the largest contributors to classification. Classification performances were similar using the SVM and Elastic Net algorithms.

Conclusions: The machine learning models used in this study were useful for classifying PET Aβ-positivity using data from routine physician visits.

Trial registration: UMIN Clinical Trials Registry (UMIN000051776, registered on 31/08/2023).

Keywords: AD; Alzheimer’s disease; Amyloid β positivity; Dementia; Machine learning.

Fig. 1

Participant disposition ^a262 records from a total of 260 unique participants CDR clinical dementia rating, MCI mild cognitive impairment, PiB-PET¹¹C-Pittsburgh Compound-B positron emission tomography

Fig. 2

Cross-validated area under the curves of amyloid β positivity classification models: L2-regularized logistic regression Model 0: demographic characteristics (age, sex, body mass index, years of education) Model 1: Model 0 plus all MMSE subscores Model 2: Model 0 plus blood test results (excluding ApoE4 phenotype) and the other demographic characteristics (medical history, current alcohol consumption, and smoking status) Model 3: Model 2 plus all MMSE subscores Model 4: Model 3 plus ApoE4 phenotype Horizontal lines are median values, upper and lower box edges show the first quartile (Q1) and the third quartile (Q3), and upper and lower whiskers represent the 1st quartile − 1.5 × the IQR and 3rd quartile + 1.5 × the IQR, respectively. Circle point is an outlier beyond the whisker range ApoE4 apolipoprotein E4, AUC area under the curve, IQR interquartile range, MMSE Mini Mental State Examination

Fig. 3

Receiver operating characteristic curve of amyloid β positivity classification Model 3: L2-regularized logistic regression AUC area under the curve, SD standard deviation

Fig. 4

Variable importance (A) and Shapley Additive exPlanations summary plot (B): L2-regularized logistic regression Model 3 ALP alkaline phosphatase, BMI body mass index, eGFR estimated glomerular filtration rate, MCV mean corpuscular volume, MMSE Mini Mental State Examination, SHAP Shapley Additive exPlanations, TSH thyroid-stimulating hormone, WBC white blood cell count