Vitamin D Deficiency in Women with Breast Cancer: A Correlation with Osteoporosis? A Machine Learning Approach with Multiple Factor Analysis
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Participants
2.2. Outcome Measures
2.3. Multiple Factor Analysis
2.4. Data Management and Statistical Analysis
3. Results
3.1. Machine Learning Results
3.2. K-Means Clustering Model Analysis
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Mean age (years) | 67.29 | ± | 8.16 |
BMI (kg/m2) | 24.4 | ± | 4.24 |
Smokers (n, %) | 17 | (31.48) | |
Grade 1 (n, %) | 9 | (16.66) | |
Grade 2 (n, %) | 34 | (62.96) | |
Grade 3 (n, %) | 11 | (20.37) | |
Surgery | |||
Quadrantectomy (n, %) | 40 | (72.07) | |
Nodulectomy (n, %) | 1 | (1.85) | |
Lumpectomy (n, %) | 3 | (5.55) | |
Mastectomy (n, %) | 10 | (18.51) | |
Radiotherapy (n, %) | 43 | (79.62) | |
Family history for osteoporotic fracture (n, %) | 10 | (18.51) | |
FN BMD (g/cm2) | 0.744 | ± | 0.10 |
FN T-score | −1.8 | ± | 0.88 |
FN Z-score | −0.4 | ± | 0.82 |
LS BMD (g/cm2) | 0.930 | ± | 0.17 |
LS T-score | −1.9 | ± | 1.25 |
LS Z-score | −0.4 | ± | 1.23 |
Osteopenia (n, %) | 23 | (42.59) | |
Osteoporosis (n, %) | 28 | (51.85) | |
[25OH-Vit.D] (ng/mL) | 19.7 | ± | 7.20 |
[25OH-Vit.D] <10 ng/mL (n, %) | 6 | (11.11) | |
[25OH-Vit.D] <20 ng/mL (n, %) | 27 | (50.00) | |
[25OH-Vit.D] <30 ng/mL (n, %) | 52 | (96.29) | |
Calcemia (mg/dL) | 9.3 | ± | 0.48 |
PTH (pg/mL) | 44.7 | ± | 12.94 |
Overall (n = 54) | [25(OH)vit.D] ≤9.9 ng/mL (n = 6) | [25(OH)vit.D] = 10–19.99 ng/mL (n = 21) | [25(OH)vit.D] = 20–29 ng/mL (n = 25) | [25(OH)vit.D] ≥30 ng/mL (n = 2) | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Osteopenia (n, %) | 23 | (43) | 1 | (2) | 12 | (22) | 8 | (15) | 2 | (4) | |||||
Osteoporosis (n, %) | 28 | (52) | 5 | (9) | 8 | (15) | 15 | (28) | 0 | (0) | |||||
FN BMD (g/cm2) | 0.744 | ± | 0.10 | 0.737 | ± | 0.12 | 0.720 | ± | 0.12 | 0.762 | ± | 0.08 | 0.798 | ± | 0.01 |
FN T-score | −1.8 | ± | 0.88 | −2.3 | ± | 0.54 | −2.1 | ± | 0.97 | −1.7 | ± | 0.87 | −1.5 | ± | 0.45 |
FN Z-score | −0.4 | ± | 0.82 | −1.2 | ± | 1.04 | −0.5 | ± | 0.75 | −0.2 | ± | 0.80 | −0.4 | ± | 0.35 |
LS BMD (g/cm2) | 0.930 | ± | 0.17 | 0.740 | ± | 0.22 | 0.938 | ± | 0.17 | 0.967 | ± | 0.15 | 0.965 | ± | 0.30 |
LS T-score | −1.9 | ± | 1.25 | −2.4 | ± | 0.79 | −1.96 | ± | 1.41 | −1.7 | ± | 1.23 | −1.6 | ± | 0.50 |
LS Z-score | −0.4 | ± | 1.23 | −1.7 | ± | 0.83 | −0.18 | ± | 1.21 | −0.1 | ± | 1.19 | −0.3 | ± | 0.01 |
25OH-Vit.D T0 (ng/mL) | 19.7 | ± | 7.20 | 7.1 | ± | 1.96 | 15.6 | ± | 2.83 | 25.1 | ± | 2.73 | 32.4 | ± | 0.07 |
Calcemia (mg/dL) | 9.3 | ± | 0.48 | 9.2 | ± | 0.41 | 9.2 | ± | 0.52 | 9.3 | ± | 0.52 | 8.9 | ± | 0.35 |
PTH (pg/mL) | 44.7 | ± | 12.94 | 47.1 | ± | 12.01 | 51.4 | ± | 15.06 | 39.7 | ± | 10.16 | 39.5 | ± | 6.36 |
r | p Value | |
---|---|---|
LS BMD (g/cm2) | 0.30 | 0.025 * |
FN BMD (g/cm2) | 0.14 | 0.300 |
Age (years) | −0.01 | 0.935 |
BMI (kg/m2) | 0.06 | 0.654 |
Calcemia (mg/dL) | 0.01 | 0.924 |
PTH (pg/mL) | −0.22 | 0.092 |
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de Sire, A.; Gallelli, L.; Marotta, N.; Lippi, L.; Fusco, N.; Calafiore, D.; Cione, E.; Muraca, L.; Maconi, A.; De Sarro, G.; et al. Vitamin D Deficiency in Women with Breast Cancer: A Correlation with Osteoporosis? A Machine Learning Approach with Multiple Factor Analysis. Nutrients 2022, 14, 1586. https://doi.org/10.3390/nu14081586
de Sire A, Gallelli L, Marotta N, Lippi L, Fusco N, Calafiore D, Cione E, Muraca L, Maconi A, De Sarro G, et al. Vitamin D Deficiency in Women with Breast Cancer: A Correlation with Osteoporosis? A Machine Learning Approach with Multiple Factor Analysis. Nutrients. 2022; 14(8):1586. https://doi.org/10.3390/nu14081586
Chicago/Turabian Stylede Sire, Alessandro, Luca Gallelli, Nicola Marotta, Lorenzo Lippi, Nicola Fusco, Dario Calafiore, Erika Cione, Lucia Muraca, Antonio Maconi, Giovambattista De Sarro, and et al. 2022. "Vitamin D Deficiency in Women with Breast Cancer: A Correlation with Osteoporosis? A Machine Learning Approach with Multiple Factor Analysis" Nutrients 14, no. 8: 1586. https://doi.org/10.3390/nu14081586