Blood predictive biomarkers for cognitive impairment among community-dwelling older adults: a cross-sectional study in China

Cognitive impairment is a common health problem. However, it is often ignored in primary healthcare due to complex examination. To develop biomarkers through easily accessible blood tests, we conducted a cross-sectional analysis including 2806 healthy Chinese adults aged over 60 years old who finished the cognition assessment though Mini-Mental State Examination as well as blood routine and biochemical examination from four communities in China between July 2020 and September 2021. The blood biomarkers commonly selected by RFE and LASSO with cross-validation in the training dataset were levels of hemoglobin, high density lipoprotein, alkaline phosphatase, direct bilirubin, globulin, creatinine, magnesium, and calcium. We developed XGBoost, logistic regression and SVM models with cross-validation in the training dataset and then evaluated their performance by the receiver operating characteristic curves, F1 score and decision curve analysis in the test dataset. The accuracies of the eight biomarkers in XGBoost, logistic regression and SVM models were 0.880 (0.846-0.915), 0.851 (0.812-0.889), and 0.852 (0.814-0.890) with confirmed clinical utility to predict cognitive impairment separately. Cognitive impairment can be predicted based on blood routine and biochemical examination with good discrimination, which could be helpful in detection and intervention at primary care.