Machine Learning-Based Routine Laboratory Tests Predict One-Year Cognitive and Functional Decline in a Population Aged 75+ Years

Abstract

Background: Cognitive and functional decline are common problems in older adults, especially in those 75+ years old. Currently, there is no specific plasma biomarker able to predict this decline in healthy old-age people. Machine learning (ML) is a subarea of artificial intelligence (AI), which can be used to predict outcomes Aim: This study aimed to evaluate routine laboratory variables able to predict cognitive and functional impairment, using ML algorithms, in a cohort aged 75+ years, in a one-year follow-up study. Method: One hundred and thirty-two older adults aged 75+ years were selected through a community-health public program or from long-term-care institutions. Their functional and cognitive performances were evaluated at baseline and one year later using a functional activities questionnaire, Mini-Mental State Examination, and the Brief Cognitive Screening Battery. Routine laboratory tests were performed at baseline. ML algorithms—random forest, support vector machine (SVM), and XGBoost—were applied in order to describe the best model able to predict cognitive and functional decline using routine tests as features. Results: The random forest model showed better accuracy than other algorithms and included triglycerides, glucose, hematocrit, red cell distribution width (RDW), albumin, hemoglobin, globulin, high-density lipoprotein cholesterol (HDL-c), thyroid-stimulating hormone (TSH), creatinine, lymphocyte, erythrocyte, platelet/leucocyte (PLR), and neutrophil/leucocyte (NLR) ratios, and alanine transaminase (ALT), leukocyte, low-density lipoprotein cholesterol (LDL-c), cortisol, gamma-glutamyl transferase (GGT), and eosinophil as features to predict cognitive decline (accuracy = 0.79). For functional decline, the most important features were platelet, PLR and NLR, hemoglobin, globulin, cortisol, RDW, glucose, basophil, B12 vitamin, creatinine, GGT, ALT, aspartate transferase (AST), eosinophil, hematocrit, erythrocyte, triglycerides, HDL-c, and monocyte (accuracy = 0.92). Conclusions: Routine laboratory variables could be applied to predict cognitive and functional decline in oldest-old populations using ML algorithms.

Graph Learning and Deep Neural Network Ensemble for Supporting Cognitive Decline Assessment

“…The methodologies vary from traditional ML approaches to advanced deep learning models, using different types of data, including genetic data, neuroimaging data, neuropsychological assessments, and, more recently, data from wearable devices [20]. In a study published by Gomes et al [21], the potential of ML algorithms to predict cognitive and functional decline in individuals aged 75 and older using routine laboratory tests was analyzed. The study applied RF, SVM, and XGBoost algorithms, identifying 20 key laboratory variables as predictors.…”

An aid diagnostic platform to detect the transition of mild cognitive impairment (MCI) to Alzheimer's disease (AD) based on 48,116 AD and MCI patients

“…Previous studies have demonstrated the potential of using routine peripheral blood parameters to predict AD risk as many parameters are altered in AD patients and some factors are associated with cognitive decline [13][14][15][16][17][18][19] . Considering the easy accessibility and cost-effectiveness of routine blood tests, routine blood analytes could complement neuropsychological tests as screening tools before conducting neuroimaging tests and detecting CSF/blood biomarkers to predict future cognitive decline in MCI subjects.…”