An Inverse Relation between Hyperglycemia and Skeletal Muscle Mass Predicted by Using a Machine Learning Approach in Middle-Aged and Older Adults in Large Cohorts
Abstract
:1. Introduction
2. Methods
2.1. Participants
2.2. Experiment Design
2.3. Training for SMM and FM Prediction Model
2.4. Verifying the Predictive Models
2.5. Predictions of SMM and FM in the Urban Hospital-Based Cohort Using the Predictive Algorithm Models
2.6. Statistical Analysis
3. Results
3.1. Metabolic Characteristics of the Ansan/Ansung and Urban Hospital-Based Cohorts
3.2. Relative Importance of Parameters in the Random Forest and XGBoost Prediction Models
3.3. Accuracies of the Predictive Models for SMM and FM Using XGBoost and ANN Algorithm in the Test Set
3.4. Anthropometric and Metabolic Parameters According to Predicted SMM and FM in Men and Women in the Urban Hospital-Based Cohort
3.5. Linear Relationship between SMM and Grip Strength in the Urban Hospital-Based Cohort
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Variables | Ansan/Ansung Cohort | Urban Hospital-Based Cohort | ||
---|---|---|---|---|
Men (3216) | Women (3441) | Men (n = 10,370) | Women (n = 20,655) | |
Age (years) | 50.6 ± 8.46 | 51.8 ± 8.88 *** | 55.2 ± 8.30 | 53.1 ± 7.75 *** |
Body mass index (kg/m2) | 24.4 ± 2.86 | 24.8 ± 3.24 *** | 24.5 ± 2.71 | 23.6 ± 2.98 *** |
Waist circumferences (cm) | 83.6 ± 7.51 | 80.6 ± 9.49 *** | 85.5 ± 7.49 | 77.8 ± 8.05 *** |
Hip circumferences (cm) | 94.4 ± 5.54 | 94.1 ± 5.96 | 95.4 ± 5.71 | 92.8 ± 5.8 *** |
Skeletal muscle mass (kg) | 37.7 ± 4.51 | 28.3 ± 3.21 *** | - | - |
Fat mass (kg) | 15.2 ± 4.81 | 19.0 ± 5.29 *** | - | - |
Grip strength (kg) | - | - | 38.5 ± 0.09 | 23.3 ± 0.04 *** |
Serum glucose (mg/dL) | 116 ± 16.3 | 115 ± 19.2 | 100 ± 22.9 | 93.8 ± 18.1 *** |
Blood HbA1c (%) | 5.78 ± 0.02 | 5.73 ± 0.01 | 5.78 ± 0.01 | 5.68 ± 0.005 *** |
SBP (mmHg) | 75.8 ± 11.4 | 72.9 ± 11.9 *** | 38.5 ± 0.09 | 23.3 ± 0.04 *** |
DBP (mmHg) | 91.1 ± 24.4 | 85.2 ± 20.3 *** | 100 ± 22.9 | 93.8 ± 18.1 *** |
Serum triglyceride (mg/dL) | 177 ± 118 | 146 ± 86.5 *** | 148 ± 102 | 114 ± 74.0 *** |
Serum HDL (mg/dL) | 43.4 ± 9.69 | 45.9 ± 10.2 *** | 50.2 ± 12.3 | 57.6 ± 13.7 *** |
Serum total cholesterol (mg/dL) | 194 ± 36.3 | 192 ± 36.2 ** | 193 ± 35.9 | 201 ± 36.3 *** |
Serum CRP (mg/dL) | 0.23 ± 0.44 | 0.21 ± 0.41 | 0.16 ± 0.44 | 0.13 ± 0.38 *** |
Serum total bilirubin (mg/dL) | 0.73 ± 0.37 | 0.54 ± 0.26 *** | 0.83 ± 0.34 | 0.67 ± 0.26 *** |
Blood platelet (103/µL) | 259 ± 64.9 | 271 ± 63.6 *** | 237 ± 54.1 | 262 ± 59.8 *** |
GFR (mL/min) | 77.7 ± 8.97 | 83.7 ± 15.7 *** | 84.3 ± 14.8 | 120 ± 20.9 *** |
Energy intake (EER%) | 96.4 ± 32.3 | 104 ± 38.6 *** | 90.8 ± 25.9 | 101 ± 32.9 *** |
CHO intake (energy%) | 69.1 ± 6.48 | 71.5 ± 6.77 *** | 71.0 ± 7.03 | 71.8 ± 7.14 *** |
Protein intake (energy%) | 13.9 ± 2.29 | 13.6 ± 2.37 *** | 13.4 ± 2.54 | 13.4 ± 2.57 |
Fat intake (energy%) | 15.7 ± 4.98 | 13.8 ± 5.26 *** | 14.5 ± 5.49 | 13.9 ± 5.56 *** |
Machine Learning Algorithm | Prediction of SMM | Prediction of FM | ||||
---|---|---|---|---|---|---|
MSE a | MAE b | R² c | MSE a | MAE b | R² c | |
Linear regression | 2.60 | 2.03 | 0.82 | 1.86 | 1.48 | 0.89 |
Support Vector Machines | 2.71 | 2.12 | 0.80 | 1.98 | 1.52 | 0.87 |
XGBoost | 2.56 | 2 | 0.82 | 1.82 | 1.43 | 0.89 |
Decision Tree | 2.81 | 2.22 | 0.78 | 2.21 | 1.75 | 0.84 |
Random Forest | 2.65 | 2.09 | 0.81 | 1.80 | 1.41 | 0.89 |
K-Nearest Neighbor (KNN) | 3.08 | 2.4 | 0.74 | 2.16 | 1.68 | 0.85 |
Artificial neural network (ANN) | 2.57 | 2 | 0.82 | 1.79 | 1.4 | 0.89 |
Metabolic Parameters | HMLF (n = 4448) | HMHF (n = 3201) | LMLF (n = 2517) | LMHF (n = 231) |
---|---|---|---|---|
Predicted SMM (kg) | 38.7 ± 0.03 b | 41.3 ± 0.05 a | 34.3 ± 0.03 d | 35.0 ± 0.06 c |
Predicted FM (%) | 22.4 ± 0.03 c | 27.3 ± 0.04 a | 20.1 ± 0.06 d | 26.6 ± 0.15 b |
Body mass index (kg/m2) | 24.1 ± 0.02 c | 27.0 ± 0.04 a | 21.8 ± 0.04 d | 24.6 ± 0.10 b |
Waist circumferences (cm) | 84.7 ± 0.08 c | 92.5 ± 0.10 a | 78.3 ± 0.11 d | 85.4 ± 0.29 b |
Hip circumferences (cm) | 95.6 ± 0.05 b | 100 ± 0.09 a | 89.2 ± 0.07 d | 91.8 ± 0.17 c |
Grip strength (kg) | 39.9 ± 0.13 a | 38.6 ± 0.17 b | 36.3 ± 0.16 c | 33.8 ± 0.54 d |
Serum glucose (mg/dL) | 98.6 ± 0.33 b | 102 ± 0.40 a | 98.6 ± 0.46 b | 102 ± 1.94 a |
Blood HBA1C (%) | 5.70 ± 0.01 b | 5.93 ± 0.01 a | 5.72 ± 0.02 b | 5.91 ± 0.06 a |
Serum triglyceride (mg/dL) | 141 ± 1.56 b | 177 ± 1.87 a | 124 ± 1.73 b | 173 ± 6.90 a |
Serum HDL (mg/dL) | 50.2 ± 0.18 b | 46.9 ± 0.18 c | 54.3 ± 0.27 a | 49.2 ± 0.82 b |
GFR (mL/min) | 84.0 ± 0.22 bc | 82.9 ± 0.27 c | 86.3 ± 0.29 a | 85.4 ± 0.93 ab |
Alcohol intake (g/day) | 39.0 ± 1.09 a | 39.8 ± 1.06 a | 29.1 ± 0.77b | 32.7 ± 3.24 b |
Metabolic parameters | HMLF (n = 5220) | HMHF (n = 10,522) | LMLF (n = 3615) | LMHF (n = 1368) |
---|---|---|---|---|
Predicted SMM (kg) | 27.8 ± 0.02 b | 29.3 ± 0.02 a | 25.0 ± 0.02 d | 25.4 ± 0.02 c |
Predicted FM (%) | 27.6 ± 0.03 c | 34.0 ± 0.03 a | 26.2 ± 0.04 d | 32.2 ± 0.06 b |
Body mass index (kg/m2) | 21.8 ± 0.02 c | 25.5 ± 0.02 a | 20.6 ± 0.03 d | 23.4 ± 0.04 b |
Waist circumferences (cm) | 74.3 ± 0.07 c | 82.6 ± 0.07 a | 69.4 ± 0.09 d | 76.6 ± 0.14 b |
Hip circumferences (cm) | 91.3 ± 0.05 b | 96.4 ± 0.05 a | 85.9 ± 0.06 d | 89.04 ± 0.08 c |
Grip strength (kg) | 24.6 ± 0.08 a | 23.3 ± 0.06 b | 22.6 ± 0.09 c | 20.5 ± 0.13 d |
Serum glucose (mg/dL) | 90.8 ± 0.19 d | 95.4 ± 0.19 b | 92.5 ± 0.28 c | 96.4 ± 0.70 a |
Blood HBA1C (%) | 5.52 ± 0.01 d | 5.76 ± 0.01 b | 5.61 ± 0.01 c | 5.85 ± 0.02 a |
Serum triglyceride (mg/dL) | 92.3 ± 0.87 d | 125 ± 0.77 b | 104 ± 1.04 c | 137 ± 2.07 a |
Serum HDL (mg/dL) | 60.3 ± 0.19 b | 55.1 ± 0.12 c | 61.2 ± 0.25 a | 55.8 ± 0.36 c |
GFR (mL/min) | 122 ± 0.28 a | 120 ± 0.21 b | 118 ± 0.34 c | 116 ± 0.59 d |
Alcohol intake (g/day) | 6.89 ± 0.21 a | 6.39 ± 0.41 a | 4.81 ± 0.23 b | 3.14 ± 0.32 c |
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Wu, X.; Park, S. An Inverse Relation between Hyperglycemia and Skeletal Muscle Mass Predicted by Using a Machine Learning Approach in Middle-Aged and Older Adults in Large Cohorts. J. Clin. Med. 2021, 10, 2133. https://doi.org/10.3390/jcm10102133
Wu X, Park S. An Inverse Relation between Hyperglycemia and Skeletal Muscle Mass Predicted by Using a Machine Learning Approach in Middle-Aged and Older Adults in Large Cohorts. Journal of Clinical Medicine. 2021; 10(10):2133. https://doi.org/10.3390/jcm10102133
Chicago/Turabian StyleWu, Xuangao, and Sunmin Park. 2021. "An Inverse Relation between Hyperglycemia and Skeletal Muscle Mass Predicted by Using a Machine Learning Approach in Middle-Aged and Older Adults in Large Cohorts" Journal of Clinical Medicine 10, no. 10: 2133. https://doi.org/10.3390/jcm10102133