NMR Spectroscopy Combined with Machine Learning Approaches for Age Prediction in Healthy and Parkinson’s Disease Cohorts through Metabolomic Fingerprints
Abstract
:1. Introduction: Background and Objective of the Work
2. Materials and Methods
2.1. Study Participants
2.2. Ethical Issues
2.3. Metabolomics Analyses
2.4. Data Processing
2.5. Serum Metabolite and Lipoprotein Quantification
2.6. Age Prediction Using Machine Learning Models
3. Results
3.1. Age Prediction Using Fingerprints and Profiles
3.2. Correlation between Predicted Ages and Disease Severity
4. Discussion and Limitations
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Cohort | Tot | N° | F/Tot | Mean Age F (Max; Min) | Mean Age M (Max; Min) |
---|---|---|---|---|---|
Hs | 118 | UNIBO (39) UMG-GOE (79) | 54/118 | 66.7 (82.5; 52) | 68.2 (85; 49) |
Cent | 57 | UNIBO (39) | 39/57 | 105.2 (112.3; 100) | 102.9 (106.3; 100) |
CentOs | 46 | UNIBO (39) | 29/46 | 70.7 (89; 55) | 71.1 (84; 58) |
Sib | 199 | AUSL-ISNB (93) SAS (106) | 115/199 | 59.8 (90; 23) | 59.2 (84; 23) |
dn2PD | 233 | UMG-GOE (228) SAS (5) | 109/233 | 65.1 (84; 29) | 64.8 (87; 39) |
advPD | 22 | UMG-GOE (22) | 7/22 | 66.7 (77; 52) | 70.0 (84; 59) |
dn2PD | advPD | ||||
---|---|---|---|---|---|
Mean | sd | Mean | sd | p-Value | |
age | 65.24 | 10.09 | 68.95 | 7.33 | 0.11 |
Hoehn and Yahr Scale | 1.51 | 1.07 | 3.13 | 0.60 | 4.52 × 10−10 |
UPDRS I | 1.63 | 1.76 | 5.05 | 3.10 | 2.74 × 10−10 |
UPDRS II | 6.83 | 5.80 | 19.45 | 6.66 | 1.98 × 10−15 |
UPDRS III | 17.42 | 13.92 | 34.41 | 15.77 | 1.11 × 10−5 |
UPDRS IV | 0.62 | 1.39 | 5.15 | 4.22 | 2.89 × 10−18 |
UPDRS sum | 25.57 | 20.28 | 62.10 | 22.08 | 2.75 × 10−11 |
Duration of the disease (years) | RD | RD | 9.32 | 2.78 | / |
BMI | 27.18 | 4.79 | 25.95 | 3.72 | 0.28 |
Model Based on Spectrum | Ctr | dn2PD | advPD | |||
---|---|---|---|---|---|---|
R2 | RMSE | R2 | RMSE | R2 | RMSE | |
SVM Linear | 0.865 | 6.273 | 0.208 | 11.209 | 0.037 | 13.057 |
ElasticNet | 0.811 | 7.466 | 0.255 | 12.488 | 0.049 | 12.789 |
PLS | 0.825 | 7.126 | 0.219 | 12.963 | 0.129 | 10.348 |
Klemera–Doubal y.true1 | 0.161 | 38.929 | 0.035 | 25.84 | 0.004 | 25.591 |
Klemera–Doubal y.true2 | 0.301 | 25.936 | 0.036 | 30.155 | 0.0001 | 29.861 |
Model Based on Metabolites | Ctr | dn2PD | advPD | |||
---|---|---|---|---|---|---|
R2 | RMSE | R2 | RMSE | R2 | RMSE | |
SVM Linear | 0.735 | 8.76 | 0.314 | 12.651 | 0.138 | 10.961 |
ElasticNet | 0.756 | 8.422 | 0.236 | 11.044 | 0.014 | 13.562 |
PLS | 0.739 | 8.704 | 0.095 | 15.157 | 0.043 | 10.423 |
Klemera–Doubal y.true1 | 0.046 | 77.305 | 0.001 | 110.239 | 0.007 | 111.439 |
Klemera–Doubal y.true2 | 0.318 | 24.903 | 0.039 | 27.304 | 0.0002 | 31.188 |
Overestimated | Underestimated | |||||||
---|---|---|---|---|---|---|---|---|
%Subj./Group | RA(m ± SD) | PA(m ± SD) | PA-RA | %Subj./Group | RA(m ± SD) | PA(m ± SD) | RA-PA | |
advPD | 31.8 | 64.29 ± 7.54 | 79.74 ± 15.49 | 15.45 ± 10.51 | 40.9 | 72.33 ± 6.58 | 60.94 ± 5.09 | 11.39 ± 4.87 |
dn2PD | 40.8 | 55.84 ± 10.81 | 70.54 ± 9.74 | 14.7 ± 6.40 | 16.7 | 74.23 ± 5.59 | 64.2 ± 6.30 | 10.01 ± 3.24 |
Ctr | 13.8 | 55.81 ± 15.78 | 66.36 ± 16.17 | 10.55 ± 4.84 | 13.3 | 84.86 ± 16.45 | 74.41 ± 16.2 | 10.44 ± 3.88 |
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Dimitri, G.M.; Meoni, G.; Tenori, L.; Luchinat, C.; Lió, P., on behalf of the PROPAG-AGEING Consortium. NMR Spectroscopy Combined with Machine Learning Approaches for Age Prediction in Healthy and Parkinson’s Disease Cohorts through Metabolomic Fingerprints. Appl. Sci. 2022, 12, 8954. https://doi.org/10.3390/app12188954
Dimitri GM, Meoni G, Tenori L, Luchinat C, Lió P on behalf of the PROPAG-AGEING Consortium. NMR Spectroscopy Combined with Machine Learning Approaches for Age Prediction in Healthy and Parkinson’s Disease Cohorts through Metabolomic Fingerprints. Applied Sciences. 2022; 12(18):8954. https://doi.org/10.3390/app12188954
Chicago/Turabian StyleDimitri, Giovanna Maria, Gaia Meoni, Leonardo Tenori, Claudio Luchinat, and Pietro Lió on behalf of the PROPAG-AGEING Consortium. 2022. "NMR Spectroscopy Combined with Machine Learning Approaches for Age Prediction in Healthy and Parkinson’s Disease Cohorts through Metabolomic Fingerprints" Applied Sciences 12, no. 18: 8954. https://doi.org/10.3390/app12188954
APA StyleDimitri, G. M., Meoni, G., Tenori, L., Luchinat, C., & Lió, P., on behalf of the PROPAG-AGEING Consortium. (2022). NMR Spectroscopy Combined with Machine Learning Approaches for Age Prediction in Healthy and Parkinson’s Disease Cohorts through Metabolomic Fingerprints. Applied Sciences, 12(18), 8954. https://doi.org/10.3390/app12188954