Descriptive and predictive analysis identify centenarians' characteristics from the Basque population

Abstract

Background: Centenarians exhibit extreme longevity and have been postulated, by some researchers, as a model for healthy aging. The identification of the characteristics of centenarians might be useful to understand the process of human aging.

Methods: In this retrospective study, we took advantage of demographic, clinical, biological, and functional data of deceased individuals between 2014 and 2020 in Guipúzcoa (Basque Country, Spain) taken from the Basque Health Service electronic health records data lake. Fifty characteristics derived from demographic, clinical, pharmaceutical, biological, and functional data were studied in the descriptive analysis and compared through differences in means tests. Twenty-seven of them were used to build machine learning models in the predictive analysis and their relevance for classifying centenarians was assessed.

Results: Most centenarians were women and lived in nursing homes. Importantly, they developed fewer diseases, took fewer drugs, and required fewer medical attendances. They also showed better biological profiles, exhibiting lower levels of glucose, hemoglobin, glycosylated hemoglobin, and triglycerides in blood analysis compared with non-centenarians. In addition, machine learning analyses revealed the main characteristics of the profiles associated with centenarians' status as being women, having fewer consultations, having fewer diagnoses of neoplasms, and having lower levels of hemoglobin.

Conclusions: Our results revealed the main characteristics linked to centenarians in the Basque Country using Computational Biology programs. These results expand the knowledge on the characterization of the centenarian population and hence of human longevity.

Keywords: biological; centenarians; description; functional; medical; predictive modeling.

Figure 1

Results from machine learning models. (A) Permutation importance of selected variables in the SVM model. Coefficients represent the decrease in the predictions when randomly shuffling the values of a given feature. (B) Performance of predictive models in centenarians and non-centenarians classification in validation datasets. (C) Logistic regression ROC curve, with an AUC of 0.87. (D) SVM ROC curve, with an AUC of 0.82.