DEMOGRAPHIC AND PHYSIOLOGICAL PREDICTORS OF GESTATIONAL DIABETES: INSIGHTS FROM LOGARITHMIC REGRESSION MODELING
Main Article Content
Keywords
Gestational diabetes mellitus, GDM prediction, logarithmic regression, maternal health, risk modelling
Abstract
Background: Gestational diabetes mellitus (GDM) poses significant risks to maternal and fetal health, necessitating practical early prediction tools. Traditional risk prediction models often fail to account for the complexity of GDM risk factors. This study explores the application of logarithmic regression to predict GDM using demographic and physiological variables.
Methods: A Cross-sectional observational study of pregnant women attending OPD Gynaecology & Obstetrics, Varun Arjun Medical College & Rohilkhand Hospital, Shahjahanpur, was analysed using logarithmic regression. Predictors included age, BMI, pregnancy number, height, weight, and heredity. Model performance was evaluated using accuracy and the area under the receiver operating characteristic (ROC) curve (AUC). Statistical significance was determined at p < 0.05.
Results: The logarithmic regression model achieved an accuracy of 75.16% and an AUC of 0.82. Significant predictors included age (p < 0.0043) and heredity (p < 0.001). Variables like pregnancy number and weight were not significant predictors (p > 0.05). The confusion matrix revealed 31 false negatives, indicating areas for improvement in classification performance.
Conclusions: Logarithmic regression demonstrated strong predictive capabilities for GDM, particularly in identifying risk factors like age and heredity. However, the model's limitations, like excluding lifestyle factors, highlight the need for further validation and refinement. Incorporating this model into clinical practice could improve early detection and intervention, reducing GDM-related complications.
Methods: A Cross-sectional observational study of pregnant women attending OPD Gynaecology & Obstetrics, Varun Arjun Medical College & Rohilkhand Hospital, Shahjahanpur, was analysed using logarithmic regression. Predictors included age, BMI, pregnancy number, height, weight, and heredity. Model performance was evaluated using accuracy and the area under the receiver operating characteristic (ROC) curve (AUC). Statistical significance was determined at p < 0.05.
Results: The logarithmic regression model achieved an accuracy of 75.16% and an AUC of 0.82. Significant predictors included age (p < 0.0043) and heredity (p < 0.001). Variables like pregnancy number and weight were not significant predictors (p > 0.05). The confusion matrix revealed 31 false negatives, indicating areas for improvement in classification performance.
Conclusions: Logarithmic regression demonstrated strong predictive capabilities for GDM, particularly in identifying risk factors like age and heredity. However, the model's limitations, like excluding lifestyle factors, highlight the need for further validation and refinement. Incorporating this model into clinical practice could improve early detection and intervention, reducing GDM-related complications.
References
1. American Diabetes Association. "Management of diabetes in pregnancy: Standards of Medical Care in Diabetes—2021." Diabetes Care 44.Supplement 1 (2021): S200-S210.
2. Metzger BE, Lowe LP, Dyer AR, et al. "Hyperglycemia and adverse pregnancy outcomes." N Engl J Med. 2008;358(19):1991-2002.
3. Kim C, Newton KM, Knopp RH. "Gestational diabetes and the incidence of type 2 diabetes: a systematic review." Diabetes Care. 2002;25(10):1862-1868.
4. Farrar D, Tuffnell DJ, West J, et al. "Continuous glucose monitoring in women with gestational diabetes to improve infant and maternal outcomes: a systematic review and meta-analysis." Diabetes Care. 2017;40(5)
5. Liu H, Wang X, Wang J. "Prediction of gestational diabetes mellitus in early pregnancy using the log-transformed body mass index." J Matern Fetal Neonatal Med. 2019;32(11):1835-1841.
6. Benhalima K, Van Crombrugge P, Moyson C, et al. "Establishing criteria for screening and diagnosis of gestational diabetes." Diabetes Care. 2020;43(2):287-291.
7. Metzger BE, Gabbe SG, Persson B, et al. "International association of diabetes and pregnancy study groups recommendations on the diagnosis and classification of hyperglycemia in pregnancy." Diabetes Care. 2010;33(3):676-682.
8. Ton J, Cleophas A, Zwinderman AH. Logarithmic transformations: a great help to statistical analyses. 2016. doi: 10.1007/978-3-319-27104-0_43.
9. Gwyn AE, Deaton A. Testing linear versus logarithmic regression models. The Review of Economic Studies. 1980. doi: 10.2307/2297113.
10. American Diabetes Association. Management of diabetes in pregnancy: Standards of Medical Care in Diabetes—2021. Diabetes Care. 2021;44(Suppl 1):S200–S210.
11. Metzger BE, Lowe LP, Dyer AR, et al. Hyperglycemia and adverse pregnancy outcomes. N Engl J Med. 2008;358(19):1991–2002.
12. Kim C, Newton KM, Knopp RH. Gestational diabetes and the incidence of type 2 diabetes: A systematic review. Diabetes Care. 2002;25(10):1862–1868.
13. Liu H, Wang X, Wang J. Prediction of gestational diabetes mellitus in early pregnancy using the log-transformed body mass index. J Matern Fetal Neonatal Med. 2019;32(11):1835–1841.
2. Metzger BE, Lowe LP, Dyer AR, et al. "Hyperglycemia and adverse pregnancy outcomes." N Engl J Med. 2008;358(19):1991-2002.
3. Kim C, Newton KM, Knopp RH. "Gestational diabetes and the incidence of type 2 diabetes: a systematic review." Diabetes Care. 2002;25(10):1862-1868.
4. Farrar D, Tuffnell DJ, West J, et al. "Continuous glucose monitoring in women with gestational diabetes to improve infant and maternal outcomes: a systematic review and meta-analysis." Diabetes Care. 2017;40(5)
5. Liu H, Wang X, Wang J. "Prediction of gestational diabetes mellitus in early pregnancy using the log-transformed body mass index." J Matern Fetal Neonatal Med. 2019;32(11):1835-1841.
6. Benhalima K, Van Crombrugge P, Moyson C, et al. "Establishing criteria for screening and diagnosis of gestational diabetes." Diabetes Care. 2020;43(2):287-291.
7. Metzger BE, Gabbe SG, Persson B, et al. "International association of diabetes and pregnancy study groups recommendations on the diagnosis and classification of hyperglycemia in pregnancy." Diabetes Care. 2010;33(3):676-682.
8. Ton J, Cleophas A, Zwinderman AH. Logarithmic transformations: a great help to statistical analyses. 2016. doi: 10.1007/978-3-319-27104-0_43.
9. Gwyn AE, Deaton A. Testing linear versus logarithmic regression models. The Review of Economic Studies. 1980. doi: 10.2307/2297113.
10. American Diabetes Association. Management of diabetes in pregnancy: Standards of Medical Care in Diabetes—2021. Diabetes Care. 2021;44(Suppl 1):S200–S210.
11. Metzger BE, Lowe LP, Dyer AR, et al. Hyperglycemia and adverse pregnancy outcomes. N Engl J Med. 2008;358(19):1991–2002.
12. Kim C, Newton KM, Knopp RH. Gestational diabetes and the incidence of type 2 diabetes: A systematic review. Diabetes Care. 2002;25(10):1862–1868.
13. Liu H, Wang X, Wang J. Prediction of gestational diabetes mellitus in early pregnancy using the log-transformed body mass index. J Matern Fetal Neonatal Med. 2019;32(11):1835–1841.
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Published
Dec 12, 2024
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How to Cite
Namita Nigam, Gaddam Thapasya Reddy, & Jyoti Ahlawat. (2024). DEMOGRAPHIC AND PHYSIOLOGICAL PREDICTORS OF GESTATIONAL DIABETES: INSIGHTS FROM LOGARITHMIC REGRESSION MODELING. Journal of Population Therapeutics and Clinical Pharmacology, 31(11), 1261-1266. https://doi.org/10.53555/d7a9xt53
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Author Biographies
Namita Nigam
Assistant Professor, Department of Obstetrics & Gynaecology, Varun Arjun Medical College & Rohilkhand Hospital
Gaddam Thapasya Reddy
Associate Professor, Department of Obstetrics & Gynaecology, Varun Arjun Medical College & Rohilkhand Hospital
Jyoti Ahlawat
PG Junior Resident, Department of Obstetrics & Gynaecology, Varun Arjun Medical College & Rohilkhand Hospital
How to Cite
Namita Nigam, Gaddam Thapasya Reddy, & Jyoti Ahlawat. (2024). DEMOGRAPHIC AND PHYSIOLOGICAL PREDICTORS OF GESTATIONAL DIABETES: INSIGHTS FROM LOGARITHMIC REGRESSION MODELING. Journal of Population Therapeutics and Clinical Pharmacology, 31(11), 1261-1266. https://doi.org/10.53555/d7a9xt53