Risk of Developing Insulin Resistance in Adult Subjects with Phenylketonuria: Machine Learning Model Reveals an Association with Phenylalanine Concentrations in Dried Blood Spots
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Design
2.2. Participant Description
2.3. Anthropometric Assessment
2.4. Biochemical Analysis
Homeostasis Model Assessment (HOMA)
2.5. Amino Acid and Acylcarnitine Determination by Tandem Mass Spectrometry (MSMS)
2.6. Statistical Analysis
2.7. Machine Learning Model
2.8. Code Availability
3. Results
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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G1 Group (n = 10) | G2 Group (n = 14) | G3 Group (n = 24) | p-Value | |
---|---|---|---|---|
Age (years) | 24 (19–27) | 24 (18–26) | 23 (19–26) | NS ** |
Sex (F/M) | 5:5 | 5:9 | 11:13 | NS *** |
Weight (kg) b | 65 (58–94) | 75 (59–81) | 68 (61–83) | NS ** |
Height (m) a | 163 ± 9 (157–170) | 163 ± 8 (158–168) | 165 ± 9 (161–168) | NS * |
BMI (kg/m2) a | 26 ± 5 (23–30) | 28 ± 7 (24–32) | 26 ± 5 (24–28) | NS * |
Waist circumference (cm) b | 82 (76–97) | 92 (78–103) | 81 (75–91) | NS ** |
G1 Group (n = 10) | G2 Group (n = 14) | G3 Group (n = 24) | p-Value | |
---|---|---|---|---|
Glycemia (mg/dL) a | 85.7 ± 5.5 (81.8–89.7) | 90.7 ± 4.9 (87.8–93.4) | 92.2 ± 8.5 (88.6–95.8) | <0.05 ɛ |
Insulin (µIU/mL) b | 7.9 (4.1–15.9) | 12.4 (10.6–19.4) | 9.6 (7.1–14.7) | <0.05 ɣ |
HOMA-IR b | 1.0 (0.5–2.0) | 1.6 (1.4–2.5) | 1.3 (0.9–1.9) | <0.05 ɣǂ |
HOMA-β (%) b | 112.2 (79.7 –61.7) | 139.1 (117.0–166.0) | 107.5 (90.3–127.5) | <0.05 ǂ |
HOMA-S (%) a | 119.5 ± 71.6 (68.3–170.7) | 60 ± 28.6 (43.6–76.7) | 82.8 ± 37.5 (66.9–98.7) | <0.05 ɣɛ |
QUICKI a | 0.36 ± 0.04 (0.33–0.39) | 0.32 ± 0.02 (0.31–0.33) | 0.34 ± 0.03 (0.33–0.35) | <0.05 ɣ |
Total cholesterol (mg/dL) a | 137.1 ± 27.6 (117.3–156.8) | 139.6 ± 27.5 (123.7–155.5) | 154.1 ± 31.1 (140.9–167.3) | NS * |
HDL cholesterol (mg/dL) a | 50.8 ± 12.8 (41.5–59.9) | 44.3 ± 8.8 (39.2–49.4) | 49.9 ± 9.3 (45.9–53.9) | NS * |
LDL cholesterol (mg/dL) b | 65.6 (48.7–82.4) | 68.3 (62.2–83.8) | 77 (60.2–99.4) | NS ** |
Triglycerides (mg/dL) b | 81.5 (54–115) | 93.5 (60–164) | 92.5 (79–122) | NS ** |
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Leal-Witt, M.J.; Rojas-Agurto, E.; Muñoz-González, M.; Peñaloza, F.; Arias, C.; Fuenzalida, K.; Bunout, D.; Cornejo, V.; Acevedo, A. Risk of Developing Insulin Resistance in Adult Subjects with Phenylketonuria: Machine Learning Model Reveals an Association with Phenylalanine Concentrations in Dried Blood Spots. Metabolites 2023, 13, 677. https://doi.org/10.3390/metabo13060677
Leal-Witt MJ, Rojas-Agurto E, Muñoz-González M, Peñaloza F, Arias C, Fuenzalida K, Bunout D, Cornejo V, Acevedo A. Risk of Developing Insulin Resistance in Adult Subjects with Phenylketonuria: Machine Learning Model Reveals an Association with Phenylalanine Concentrations in Dried Blood Spots. Metabolites. 2023; 13(6):677. https://doi.org/10.3390/metabo13060677
Chicago/Turabian StyleLeal-Witt, María Jesús, Eugenia Rojas-Agurto, Manuel Muñoz-González, Felipe Peñaloza, Carolina Arias, Karen Fuenzalida, Daniel Bunout, Verónica Cornejo, and Alejandro Acevedo. 2023. "Risk of Developing Insulin Resistance in Adult Subjects with Phenylketonuria: Machine Learning Model Reveals an Association with Phenylalanine Concentrations in Dried Blood Spots" Metabolites 13, no. 6: 677. https://doi.org/10.3390/metabo13060677
APA StyleLeal-Witt, M. J., Rojas-Agurto, E., Muñoz-González, M., Peñaloza, F., Arias, C., Fuenzalida, K., Bunout, D., Cornejo, V., & Acevedo, A. (2023). Risk of Developing Insulin Resistance in Adult Subjects with Phenylketonuria: Machine Learning Model Reveals an Association with Phenylalanine Concentrations in Dried Blood Spots. Metabolites, 13(6), 677. https://doi.org/10.3390/metabo13060677