Machine Learning Approximations to Predict Epigenetic Age Acceleration in Stroke Patients
Abstract
:1. Introduction
2. Results
2.1. Descriptive Analysis
2.2. Model Training
2.3. Model Evaluation
2.4. Model Interpretation
3. Discussion
4. Materials and Methods
4.1. Setting
4.2. Ethics
4.3. Clinical Variables
4.3.1. Vascular Risk Factors
4.3.2. Lifestyle and Diet
4.3.3. Target Organ Damage
4.4. Age Acceleration Estimation
4.4.1. Array-Based DNA Methylation Quantification
4.4.2. Biological Age
4.5. Statistical Analyses
4.5.1. Descriptive Statistics
4.5.2. Missing Data, Imputation, and Data Pre-Processing
4.5.3. Model Training
4.5.4. Model Evaluation
4.5.5. Model Interpretation
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Demographic Variables | |
---|---|
Age, years | 72.68 (±12.4) |
Biological Age, years | 73.76 (±10.4) |
Age Acceleration, years | 1.081 (±7.3) |
Sex, Female | 404 (42.4%) |
Morphometric Measurements | |
Weight, kg | 73.3 (±13.4) |
Height, cm | 163.7 (±9.2) |
BMI, kg/m2 | 26.7 (±4.6) |
Vascular Risk Factors | |
Hypertension | 701 (73.6%) |
Diabetes | 352 (37.0%) |
Hyperlipidemia | 448 (47.1%) |
Ischemic heart disease | 128 (13.4%) |
Atrial fibrillation | 293 (30.8%) |
Laboratory Determinations | |
Leukocytes, u/mcL | 8917 (±2937) |
Neutrophils, u/mcL | 6285 (±2732) |
Lymphocytes, % | 21.8 (±9.3) |
Monocytes, % | 6 (±2.2) |
Total cholesterol, mg/dL | 174.2 (±41) |
Triglycerides, mg/dL | 124.5 (±63.2) |
HDL, mg/dL | 47.1 (±12.5) |
LDL, mg/dL | 103.1 (±34.2) |
Lifestyle | |
Active smoker | 284 (29.8%) |
Alcoholism | |
No | 673 (70.7%) |
Previous alcoholism > 1 year | 42 (4.41%) |
Yes | 237 (24.9%) |
Drug consumption | 22 (2.31%) |
Previous Functional Status | |
Baseline mRS | |
0 | 611 (64.2%) |
1 | 113 (11.9%) |
2 | 100 (10.5%) |
3 | 93 (9.8%) |
4 | 33 (3.5%) |
5 | 2 (0.2%) |
Variable | Correlation® | Average Age-A (±SD) | p-Value |
---|---|---|---|
Demographic Variables | |||
Age, years | −0.55 | <0.001 * | |
Biological Age, years | 0.05 | 0.1273 | |
Sex: | <0.001 * | ||
Female | −1.22 (±6.6) | ||
Male | 2.78 (±7.27) | ||
Morphometric Measurements | |||
Weight (kg) | 0.23 | <0.001 * | |
Height (cm) | 0.3 | <0.001 * | |
BMI (kg/m2) | 0.07 | 0.02 * | |
Vascular Risk Factors | |||
Hypertension | 0.78 | ||
No | 1.19 (±7.23) | ||
Yes | 1.04 (±7.28) | ||
Diabetes | 0.952 | ||
No | 1.07 (±7.39) | ||
Yes | 1.1 (±7.06) | ||
Hyperlipidemia | 0.229 | ||
No | 0.81 (±7.60) | ||
Yes | 1.38 (±6.86) | ||
Ischemic heart disease | 0.01 * | ||
No | 1.3 (±7.34) | ||
Yes | −0.36 (±6.59) | ||
Atrial fibrillation | <0.001 * | ||
No | 1.98 (±7.26) | ||
Yes | −0.93 (±6.87) | ||
Laboratory Determinations | |||
Leukocytes, u/mcL | 0.14 | <0.001 * | |
Neutrophils, u/mcL | 0.13 | <0.001 * | |
Lymphocytes, % | −0.02 | 0.517 | |
Monocytes, % | −0.004 | 0.898 | |
Total cholesterol, mg/dL | −0.008 | 0.803 | |
Triglycerides, mg/dL | 0.12 | <0.001 * | |
HDL, mg/dL | −0.15 | <0.001 * | |
LDL, mg/dL | 0.05 | 0.0868 | |
Lifestyle | |||
Smoking | <0.001 * | ||
No | −0.29 (±6.93) | ||
Yes | 4.31 (±7.00) | ||
Alcoholism | <0.001 * | ||
No | −0.19 (±7.22) | ||
Previous alcoholism > 1 year | 1.57 (±5.35) | ||
Yes | 4.6 (±6.49) | ||
Drug consumption | <0.001 * | ||
No | 0.97 (±7.27) | ||
Yes | 5.88 (±4.92) | ||
Previous Functional Status | |||
Baseline mRS | −0.22 | <0.001 * |
r | R² | RMSE | MAE | ICCC | |
---|---|---|---|---|---|
Linear Regression | 0.583 | 0.34 | 5.969 | 4.287 | 0.51 |
Elastic net | 0.598 | 0.358 | 5.917 | 4.248 | 0.49 |
K nearest-neighbors | 0.516 | 0.266 | 6.401 | 4.747 | 0.37 |
Random Forest | 0.579 | 0.335 | 5.992 | 4.371 | 0.49 |
Support Vector Machine | 0.584 | 0.341 | 6.083 | 4.467 | 0.44 |
Multi-Layer Perceptron | 0.615 | 0.378 | 5.852 | 4.263 | 0.52 |
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Fernández-Pérez, I.; Jiménez-Balado, J.; Lazcano, U.; Giralt-Steinhauer, E.; Rey Álvarez, L.; Cuadrado-Godia, E.; Rodríguez-Campello, A.; Macias-Gómez, A.; Suárez-Pérez, A.; Revert-Barberá, A.; et al. Machine Learning Approximations to Predict Epigenetic Age Acceleration in Stroke Patients. Int. J. Mol. Sci. 2023, 24, 2759. https://doi.org/10.3390/ijms24032759
Fernández-Pérez I, Jiménez-Balado J, Lazcano U, Giralt-Steinhauer E, Rey Álvarez L, Cuadrado-Godia E, Rodríguez-Campello A, Macias-Gómez A, Suárez-Pérez A, Revert-Barberá A, et al. Machine Learning Approximations to Predict Epigenetic Age Acceleration in Stroke Patients. International Journal of Molecular Sciences. 2023; 24(3):2759. https://doi.org/10.3390/ijms24032759
Chicago/Turabian StyleFernández-Pérez, Isabel, Joan Jiménez-Balado, Uxue Lazcano, Eva Giralt-Steinhauer, Lucía Rey Álvarez, Elisa Cuadrado-Godia, Ana Rodríguez-Campello, Adrià Macias-Gómez, Antoni Suárez-Pérez, Anna Revert-Barberá, and et al. 2023. "Machine Learning Approximations to Predict Epigenetic Age Acceleration in Stroke Patients" International Journal of Molecular Sciences 24, no. 3: 2759. https://doi.org/10.3390/ijms24032759