Using machine learning to study the association of sociodemographic indicators, biomarkers, and oral condition in older adults in Colombia.

Background: Chronic health conditions and socioeconomic problems that affect the well-being and life expectancy of older adults are common. The objective of this cross-sectional study was to analyze the association between sociodemographic variables, oral conditions, and general health and the biomarkers of older adults using machine learning (ML).

Methods: A total of 15,068 surveys from the national study of Health, Well-Being and Aging (Salud, Bienestar y Envejecimiento) data set were used for this secondary analysis. Of these, 3,128 people provided blood samples for the analysis of blood biomarkers. Sociodemographic, oral health, and general health variables were analyzed using ML and logistic regression.

Results: The results of clustering analysis showed that dyslipidemia was associated with poor oral condition, lower socioeconomic status, being female, and low education. The self-perception of oral health in older adults was not associated with the presence of teeth, blood biomarkers, or socioeconomic variables. However, the necessity of replacing a dental prosthesis was associated with the lowest self-perception of oral health. Edentulism was associated with being female, increased age, and smoking.

Conclusions: Socioeconomic and educational disparities, sex, and smoking are important factors for tooth loss and suboptimal blood biomarkers in older adults. ML is a powerful tool for identifying potential variables that may aid in the prevention of systemic and oral diseases in older adults, which would improve geriatric dentistry.

Practical Implications: These findings can help the academic community identify critical sociodemographic and clinical factors that influence the process of healthy aging and serve as a useful guide to enhance health care policies and geriatric oral health care services.