## 4 Discussion and Limitations  
Biological aging can be defined as a collection of interconnected molecular and cellular changes associated with aging, and it is thought to induce physiological degeneration, which is at the root of a variety of age-related health disorders, according to the emerging area of geroscience. Indeed, while some people get age-related diseases such as Parkinson's disease, others, such as centenarians, can live to 100 years old and beyond in good health. There is a spectrum of intermediate phenotypes between these two extremes, including those with more or less pronounced subclinical signs of disorders [21]. In this study, we decided to evaluate the biological age of a large population of healthy subjects, centenarians and their offspring, siblings of PD patients, and two groups of PD patients including de novo drug-naive PD patients and advanced PD patients under dopaminergic treatment. To our knowledge, this is the first time that a metabolomic-based age estimation has been utilized to assess the age of Parkinson's disease patients. The model was developed using data from a collection of healthy non-PD cohorts and then tested using data from the de novo drug-naive and advanced PD patients to assess how large is the difference between actual and predicted age in PD patients and in healthy subjects. Four algorithms were used for the prediction of biological aging: ElasticNet, PLS, SVM, and Klemera-Doubal. SVM resulted to be the best-performing one, especially when coupled with the NMR fingerprint approach. Interestingly, we found that the predictive model built on healthy subjects shows a quite good agreement between the actual and the predicted chronological age. Conversely, when we attempted to predict the age of PD patients, the model showed a larger discrep  
Figure 3: [PERSON]’s correlation coefficients among predicted age (estimated using PLS), severity scores, and other demographic and physiological variables. Data are obtained using the dn\({}^{2}\)PD cohort. Asterisks represent \(p\)-values \(<\) 0.05.  
ancy. Moreover, the error is larger in dn\({}^{2}\)PD patients than in advPD patients. Reasonably, this could be due to the presence of the dopaminergic treatment in the advPD group, which interferes with and/or reduces the gap between chronological and predicted age. Further, it seems that working with a larger data matrix, such as the full NMR spectra, works better than working using only some components of the spectra. The full NMR spectrum is considered the \"fingerprint\" of all (assigned or unassigned) detectable metabolites and lipoproteins present in that biological sample. Indeed, the fingerprinting approach is essentially utilized in metabolomics to provide sample classification. In contrast, the determination of the concentrations of all quantifiable metabolites in a biological sample, defined as \"profiling\", is generally used to provide information regarding the metabolic pathways involved in specific pathological or physiological conditions [24]. However, the molecules that can be quantified by profiling are far fewer than those that contribute to the fingerprint. Therefore, it was not completely unexpected that the fingerprinting approach performed better in sample classification. Interestingly, we found a correlation between the predicted chronological age and the severity scores (Hoehn and Yahr scale and UPDRS) in advPD patients. This correlation is stronger than that calculated from the actual chronological age. This evidence suggests a possible use of metabolomics in the determination of a biological aging process that is also descriptive of the progress of PD. However, one of the main limitations of the study is the limited number of advPD patients. Further, linking the metabolomic-based age estimation to the altered metabolic pathways could improve our understanding of the aging process in healthy people and patients with cognitive disorders.