Automated Machine Learning (AutoML)-Derived Preconception Predictive Risk Model to Guide Early Intervention for Gestational Diabetes Mellitus
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Design
2.2. Machine Learning Methodology and Statistical Analyses
3. Results
3.1. Top Predictors from AutoML Feature Selection Model
3.2. Preconception Predictive Risk Model
3.3. Associations of Top Predictors and GDM Outcome
3.4. Associations of Top Predictors and Adverse Birth Outcomes (Preterm Birth, Low Birthweight at Term and Large for Gestational Age Infant)
4. Discussion
Primary Findings
5. Limitations
Comparison with Prior Work
6. 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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S-PRESTO (n = 222) | |
---|---|
Demographics | |
Age (years), mean ± SD | 30.51 ± 3.11 |
Ethnicity, n (%) | |
Chinese | 176 (79.28) |
Malay | 30 (13.51) |
Indian | 16 (7.21) |
Medical/Obstetric History | |
Family history of diabetes mellitus, n (%) | |
Yes | 63 (28.38) |
No | 159 (71.62) |
History of GDM, n (%) | |
Yes | 6 (2.70) |
No | 216 (97.30) |
Parity, n (%) | |
Nulliparous | 140 (63.06) |
Multiparous | 82 (36.94) |
Medical history of high blood pressure, n (%) | |
Yes | 0 (0.00) |
No | 222 (100.00) |
Physical Measures at Preconception | |
Pre-pregnancy weight (kg), mean ± SD | 59.31 ± 11.80 |
Maternal height (cm), mean ± SD | 159.96 ± 5.55 |
Pre-pregnancy BMI (kg/m2), mean ± SD | 23.18 ± 4.52 |
Waist circumference (cm), mean ± SD | 81.35 ± 10.10 |
Mid-upper arm circumference (cm), mean ± SD | 27.27 ± 4.08 |
Systolic blood pressure (mm Hg), mean ± SD | 104.15 ± 8.92 |
Diastolic blood pressure (mm Hg), mean ± SD | 67.38 ± 7.51 |
Mean arterial blood pressure (mm Hg), mean ± SD | 79.63 ± 7.48 |
Blood-Derived Markers at Preconception | |
HbA1c (mmol/mol), mean ± SD | 31.80 ± 2.73 |
Fasting glucose (mmol/L), mean ± SD | 4.72 ± 0.33 |
Fasting insulin (mU/L), mean ± SD | 5.97 ± 4.83 |
Triglycerides (mmol/L), mean ± SD | 0.81 ± 0.38 |
High density lipoprotein cholesterol (mmol/L), mean ± SD | 1.48 ± 0.28 |
Gamma-glutamyl transferase (U/L), mean ± SD | 18.99 ± 14.28 |
Lifestyle Factors at Preconception | |
Self-reported smoking, n (%) | |
Yes | 6 (2.70) |
No | 216 (97.30) |
Self-reported alcohol consumption, n (%) | |
Yes | 159 (71.62) |
No | 63 (28.38) |
Metabolic Indices at Preconception | |
Homeostasis model assessment-insulin resistance (HOMA-IR) index, mean ± SD | 1.27 ± 1.08 |
Triglycerides/high density lipoprotein cholesterol ratio | 0.59 ± 0.41 |
Fatty liver index, mean ± SD | 5.61 ± 10.38 |
Metabolic syndrome, n (%) | |
Yes | 7 (3.15) |
No | 215 (96.85) |
Prediabetes Status at Preconception | |
Impaired fasting glucose (IFG), n (%) | 0 (0.00) |
Impaired glucose tolerance (IGT), n (%) | 11 (5.00) |
Type 2 diabetes (T2D), n (%) | 0 (0.00) |
Normal glucose metabolism, n (%) | 209 (95.00) |
OGTT at 24+1–28+6 Weeks’ Gestation | |
Glucose measures (mmol/L), mean ± SD | |
Fasting glucose | 4.28 ± 0.35 |
1-hour glucose | 7.99 ± 1.52 |
2-hour glucose | 6.68 ± 1.27 |
GDM, n (%) | |
IADPSG/WHO 2013 criteria | 29 (13.06) |
Adverse Birth Outcomes | |
Preterm birth, n (%) | |
Yes | 10 (4.50) |
No | 212 (95.50) |
Low birthweight at term, n (%) | |
Yes | 7 (3.24) |
No | 209 (96.76) |
Large for gestational age infant, n (%) | |
Yes | 34 (15.74) |
No | 182 (84.26) |
Features | Optimal Machine Learning Pipeline | AUC |
---|---|---|
1: HbA1c | Gradient boosting classifier. | 0.81 |
2: HbA1c + fatty liver index | Stacked ensemble model with logistic regression classifier, multinomial naïve Bayes classifier and multi-layer perceptron classifier. | 0.78 |
3: HbA1c + fatty liver index + mean arterial blood pressure | Stacked ensemble model with k-nearest neighbors classifier and decision tree classifier. | 0.82 |
4: HbA1c + fatty liver index + mean arterial blood pressure + fasting insulin | Stacked ensemble model with k-nearest neighbors classifier and decision tree classifier. | 0.88 |
5: HbA1c + fatty liver index + mean arterial blood pressure + fasting insulin + TG/HDL ratio | Extra trees classifier. | 0.93 |
6: HbA1c + fatty liver index + mean arterial blood pressure + fasting insulin + TG/HDL ratio + height | Stacked ensemble model with logistic regression classifier (stochastic gradient descent training) and k-nearest neighbors classifier. | 0.89 |
7: HbA1c + fatty liver index + mean arterial blood pressure + fasting insulin + TG/HDL ratio + height + age | Multi-layer perceptron classifier. | 0.88 |
8: HbA1c + fatty liver index + mean arterial blood pressure + fasting insulin + TG/HDL ratio + height + age + mid-upper arm circumference | Stacked ensemble model with Bernoulli naïve Bayes classifier, gaussian naïve Bayes classifier, multinomial naïve Bayes classifier and linear support vector machine classifier. | 0.93 |
9: HbA1c + fatty liver index + mean arterial blood pressure + fasting insulin + TG/HDL ratio + height + age + mid-upper arm circumference + BMI | Stacked ensemble model with extra trees classifier, Bernoulli naïve Bayes classifier and gaussian naïve Bayes classifier. | 0.85 |
10: HbA1c + fatty liver index + mean arterial blood pressure + fasting insulin + TG/HDL ratio + height + age + mid-upper arm circumference + BMI + parity | Stacked ensemble model with k-nearest neighbors classifier and multi-layer perceptron classifier. | 0.85 |
11: HbA1c + fatty liver index + mean arterial blood pressure + fasting insulin + TG/HDL ratio + height + age + mid-upper arm circumference + BMI + parity + alcohol consumption | Stacked ensemble model with gradient boosting classifier, multi-layer perceptron classifier and linear support vector machine classifier. | 0.90 |
12: HbA1c + fatty liver index + mean arterial blood pressure + fasting insulin + TG/HDL ratio + height + age + mid-upper arm circumference + BMI + parity + alcohol consumption + family history of diabetes mellitus | Stacked ensemble model with multinomial naïve Bayes classifier and multi-layer perceptron classifier. | 0.87 |
13: HbA1c + fatty liver index + mean arterial blood pressure + fasting insulin + TG/HDL ratio + height + age + mid-upper arm circumference + BMI + parity + alcohol consumption + family history of diabetes mellitus + Chinese ethnicity | Stacked ensemble model with multinomial naïve Bayes classifier and multi-layer perceptron classifier. | 0.87 |
14: Mean arterial blood pressure + height + age + mid-upper arm circumference + BMI + parity + alcohol consumption + family history of diabetes mellitus + Chinese ethnicity | Stacked ensemble model with linear support vector machine classifier (stochastic gradient descent training), Bernoulli naïve Bayes classifier, multinomial naïve Bayes classifier, multi-layer perceptron classifier and linear support vector machine classifier. | 0.81 |
Feature | GDM (n = 222) |
---|---|
OR (95% CI) p-Value | |
HbA1c (mmol/mol) | OR: 1.31 (1.12–1.53) p-value = 0.001 * |
Fatty liver index | OR: 1.01 (0.98–1.05) p-value = 0.458 |
Mean arterial blood pressure (mm Hg) | OR: 0.99 (0.94–1.04) p-value = 0.584 |
Fasting insulin (mU/L) | OR: 1.05 (0.99–1.12) p-value = 0.119 |
Triglycerides/high density lipoprotein cholesterol ratio | OR: 1.45 (0.65–3.28) p-value = 0.365 |
Maternal height (cm) | OR: 0.96 (0.90–1.04) p-value = 0.311 |
Age (years) | OR: 0.97 (0.86–1.10) p-value = 0.673 |
Mid-upper arm circumference (cm) | OR: 1.05 (0.96–1.15) p-value = 0.290 |
BMI (kg/m2) | OR: 1.05 (0.97–1.13) p-value = 0.241 |
Parity | OR: 0.74 (0.32–1.71) p-value = 0.481 |
Self-reported alcohol consumption | OR: 2.06 (0.75–5.67) p-value = 0.161 |
Family history of diabetes mellitus | OR: 1.39 (0.61–3.18) p-value = 0.436 |
Chinese vs. Malay/Indian ethnicity | OR: 1.29 (0.47–3.60) p-value = 0.621 |
Feature | GDM (n = 222) |
OR (95% CI) p-value | |
HbA1c (mmol/mol) ^ | OR: 1.34 (1.13–1.60) p-value = 0.001 * |
Feature | GDM (n = 211) |
OR (95% CI) p-value | |
HbA1c (mmol/mol) #,^ | OR: 1.32 (1.10–1.59) p-value = 0.003 * |
Feature | Preterm Birth (n = 222) | Low Birthweight at Term (n = 216) | Large for Gestational Age Infant (n = 216) |
---|---|---|---|
OR (95% CI) p-Value | OR (95% CI) p-Value | OR (95% CI) p-Value | |
HbA1c (mmol/mol) | OR: 1.28 (1.01–1.62) p-value = 0.042 * | OR: 1.13 (0.86–1.49) p-value = 0.381 | OR: 1.06 (0.92–1.21) p-value = 0.416 |
Fatty liver index | OR: 1.00 (0.94–1.06) p-value = 0.951 | OR: 0.89 (0.68–1.16) p-value = 0.386 | OR: 1.06 (1.03–1.10) p-value < 0.001 * |
Mean arterial blood pressure (mm Hg) | OR: 1.02 (0.94–1.11) p-value = 0.688 | OR: 0.96 (0.86–1.06) p-value = 0.403 | OR: 1.03 (0.98–1.08) p-value = 0.253 |
Fasting insulin (mU/L) | OR: 1.04 (0.96–1.14) p-value = 0.317 | OR: 1.05 (0.95–1.15) p-value = 0.359 | OR: 1.08 (1.01–1.16) p-value = 0.019 * |
Triglycerides/high density lipoprotein cholesterol ratio | OR: 0.79 (0.13–4.76) p-value = 0.797 | OR: 1.42 (0.34–6.00) p-value = 0.630 | OR: 2.85 (1.30–6.21) p-value = 0.009 * |
Maternal height (cm) | OR: 0.95 (0.84–1.07) p-value = 0.363 | OR: 0.91 (0.78–1.05) p-value = 0.192 | OR: 0.99 (0.93–1.06) p-value = 0.794 |
Age (years) | OR: 1.05 (0.86–1.29) p-value = 0.629 | OR: 0.97 (0.76–1.24) p-value = 0.799 | OR: 1.07 (0.95–1.20) p-value = 0.267 |
Mid-upper arm circumference (cm) | OR: 0.97 (0.82–1.15) p-value = 0.737 | OR: 0.90 (0.71–1.14) p-value = 0.362 | OR: 1.22 (1.12–1.33) p-value < 0.001 * |
BMI (kg/m2) | OR: 1.00 (0.88–1.16) p-value = 0.914 | OR: 0.84 (0.64–1.10) p-value = 0.206 | OR: 1.18 (1.09–1.27) p-value < 0.001 * |
Parity | OR: 1.75 (0.49–6.25) p-value = 0.387 | OR: 1.29 (0.28–5.90) p-value = 0.746 | OR: 1.89 (0.90–3.95) p-value = 0.091 |
Self-reported alcohol consumption | OR: 0.38 (0.11–1.35) p-value = 0.134 | OR: 2.47 (0.29–20.97) p-value = 0.407 | OR: 0.44 (0.21–0.94) p-value = 0.033 * |
Family history of diabetes mellitus | OR: 1.73 (0.47–6.35) p-value = 0.409 | OR: 1.91 (0.41–8.78) p-value = 0.407 | OR: 1.95 (0.92–4.17) p-value = 0.083 |
Chinese vs. Malay/Indian ethnicity | OR: 0.59 (0.15–2.39) p-value = 0.463 | OR: 0.33 (0.07–1.51) p-value = 0.152 | OR: 0.55 (0.24–1.26) p-value = 0.158 |
Feature | Preterm Birth (n = 185) | ||
OR (95% CI) p-value | |||
HbA1c (mmol/mol) ^ | OR: 1.63 (1.12–2.38) p-value = 0.011 * | ||
Feature | Preterm Birth (n = 154) | ||
OR (95% CI) p-value | |||
HbA1c (mmol/mol) #,^ | OR: 1.75 (1.14–2.67) p-value = 0.010 * | ||
Feature | Large for Gestational Age Infant (n = 198) | ||
OR (95% CI) p-value | |||
Fatty liver index ^ | OR: 1.02 (0.96–1.08) p-value = 0.473 | ||
Feature | Large for Gestational Age Infant (n = 198) | ||
OR (95% CI) p-value | |||
Fasting insulin (mU/L) ^ | OR: 1.01 (0.92–1.10) p-value = 0.825 | ||
Feature | Large for Gestational Age Infant (n = 198) | ||
OR (95% CI) p-value | |||
Triglycerides/high density lipoprotein cholesterol ratio ^ | OR: 1.98 (0.76–5.10) p-value = 0.160 | ||
Feature | Large for Gestational Age Infant (n = 198) | ||
OR (95% CI) p-value | |||
Mid-upper arm circumference (cm) ^ | OR: 1.21 (0.93–1.58) p-value = 0.162 | ||
Feature | Large for Gestational Age Infant (n = 198) | ||
OR (95% CI) p-value | |||
BMI (kg/m2) ~ | OR: 1.20 (1.10–1.31) p-value < 0.001 * | ||
Feature | Large for Gestational Age Infant (n = 198) | ||
OR (95% CI) p-value | |||
Self-reported alcohol consumption ^ | OR: 0.47 (0.17–1.28) p-value = 0.138 |
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Kumar, M.; Ang, L.T.; Png, H.; Ng, M.; Tan, K.; Loy, S.L.; Tan, K.H.; Chan, J.K.Y.; Godfrey, K.M.; Chan, S.-y.; et al. Automated Machine Learning (AutoML)-Derived Preconception Predictive Risk Model to Guide Early Intervention for Gestational Diabetes Mellitus. Int. J. Environ. Res. Public Health 2022, 19, 6792. https://doi.org/10.3390/ijerph19116792
Kumar M, Ang LT, Png H, Ng M, Tan K, Loy SL, Tan KH, Chan JKY, Godfrey KM, Chan S-y, et al. Automated Machine Learning (AutoML)-Derived Preconception Predictive Risk Model to Guide Early Intervention for Gestational Diabetes Mellitus. International Journal of Environmental Research and Public Health. 2022; 19(11):6792. https://doi.org/10.3390/ijerph19116792
Chicago/Turabian StyleKumar, Mukkesh, Li Ting Ang, Hang Png, Maisie Ng, Karen Tan, See Ling Loy, Kok Hian Tan, Jerry Kok Yen Chan, Keith M. Godfrey, Shiao-yng Chan, and et al. 2022. "Automated Machine Learning (AutoML)-Derived Preconception Predictive Risk Model to Guide Early Intervention for Gestational Diabetes Mellitus" International Journal of Environmental Research and Public Health 19, no. 11: 6792. https://doi.org/10.3390/ijerph19116792