Novel definition of time range and risk factors of pregnant women with gestational diabetes mellitus detected early in pregnancy a cluster analysis using clinical data of the German GestDiab cohort – Diabetology & Metabolic Syndrome

Report on the Classification and Prediction of Gestational Diabetes Mellitus in Alignment with Sustainable Development Goals

Introduction: Enhancing Maternal Health through Data-Driven Insights

This report details the findings of a study on Gestational Diabetes Mellitus (GDM), focusing on its classification, risk factors, and the development of a predictive model. The research directly supports the achievement of several United Nations Sustainable Development Goals (SDGs). By improving the understanding and early detection of GDM, this work contributes significantly to SDG 3: Good Health and Well-being, which aims to ensure healthy lives and promote well-being for all ages, with a specific focus on reducing maternal mortality and preventing non-communicable diseases. Furthermore, by addressing a health condition exclusive to women, the study reinforces the objectives of SDG 5: Gender Equality, promoting women’s health and well-being.

Analysis and Classification of GDM Onset

Cluster Analysis for Diagnostic Refinement

A temporal analysis of GDM diagnosis frequency revealed two distinct peaks, prompting a data-driven classification of patients. Using a k-means clustering algorithm (k=2), a decision boundary was established at approximately week 20.88 of gestation. This methodology allows for a more precise classification of GDM, which is critical for tailoring clinical interventions and aligns with the preventative care principles of SDG 3.

• Early GDM (eGDM): Diagnosis before week 20.88 of pregnancy. This group comprised 1,639 patients.
• Standard GDM (sGDM): Diagnosis after week 20.88 of pregnancy. This group comprised 16,856 patients.

Identification of Key Risk Factors for Early GDM

Maternal Characteristics and Biometric Indicators

A comparative analysis of the two GDM cohorts identified several significant risk factors for the early onset of the condition. Identifying these factors is crucial for creating targeted public health strategies that address health disparities, thereby supporting SDG 10: Reduced Inequalities, as many of these factors are linked to socioeconomic determinants of health.

1. Pre-pregnancy Body Weight and BMI: This emerged as a major risk factor. Women in the eGDM group had a significantly higher average pre-pregnancy weight (86.65 kg vs. 76.81 kg) and BMI (31.15 kg/m² vs. 27.99 kg/m²). The incidence of eGDM increased linearly with weight class, peaking at 19.95% in women with class 3 obesity (BMI > 40 kg/m²).
2. History of Previous GDM: This was the strongest categorical predictor. Of women with a history of GDM, 28.78% developed eGDM, compared to only 4.67% of women without a prior history.
3. Fasting Glucose Levels: The fasting Oral Glucose Tolerance Test (OGTT) value was a strong numerical predictor, being significantly higher in the eGDM cohort (98.064 mg/dl vs. 94.663 mg/dl).
4. Gravidity and Parity: Both the number of previous pregnancies (gravidity) and live births (parity) were significantly higher in the eGDM cohort.

Factors such as maternal age and family history of diabetes mellitus did not show a statistically significant association with the development of eGDM.

Development of a Predictive Model for Early GDM

Binary Logistic Regression Model

To advance preventative healthcare in line with SDG 3, a binary logistic regression model was developed to classify pregnancies into eGDM versus sGDM categories. The model incorporates nine key predictor variables:

• Pre-pregnancy BMI
• Maternal age
• GDM in a previous pregnancy
• Family history of diabetes mellitus
• Fasting plasma glucose (FPG)
• 1-hour plasma glucose value
• 2-hour plasma glucose value
• Gravidity
• Parity

Model Performance and Predictive Importance

The model’s effectiveness was quantified using odds ratios (OR) and evaluated with a Receiver Operating Characteristic (ROC) curve. This data-driven approach provides a powerful tool for healthcare providers to identify at-risk individuals early.

• Significant Predictors: A history of GDM in a previous pregnancy was highly significant. Increased BMI (OR: 1.042), fasting plasma glucose (OR: 1.022), and maternal age (OR 1.023) were also associated with an increased risk of eGDM.
• Model Accuracy: The model demonstrated moderately good classification performance, achieving an Area Under the Curve (AUC) of 0.83 (95% CI: 0.8027–0.857), with a sensitivity of 0.768 and a specificity of 0.779.

Conclusion and Public Health Implications

Summary of Findings and Contribution to Sustainable Development

This study successfully validates the classification of GDM diagnosed before the 21st week of gestation as a distinct clinical entity (eGDM). The research identifies high pre-pregnancy BMI, a history of previous GDM, and elevated fasting glucose as primary risk factors. The development of a predictive model with an AUC of 0.83 provides a valuable clinical tool for early identification, directly contributing to the goals of SDG 3 by promoting preventative medicine and improving maternal health outcomes. The findings suggest that a fasting plasma glucose level of 98 mg/dl could serve as an effective screening threshold for eGDM. By highlighting modifiable risk factors like body weight, this report underscores the need for public health policies that address health inequalities (SDG 10) and prioritize women’s health (SDG 5).

Analysis of Sustainable Development Goals

1. Which SDGs are addressed or connected to the issues highlighted in the article?

• SDG 3: Good Health and Well-being: The article is fundamentally a medical research paper focused on Gestational Diabetes Mellitus (GDM), a significant health issue affecting pregnant women. It investigates the risk factors, diagnosis, and prediction of GDM, which directly aligns with the goal of ensuring healthy lives and promoting well-being for all at all ages, particularly for maternal health and the prevention of non-communicable diseases (NCDs).

Specific Targets and Indicators

2. What specific targets under those SDGs can be identified based on the article’s content?

• Target 3.4: By 2030, reduce by one third premature mortality from non-communicable diseases through prevention and treatment and promote mental health and well-being.
  • Explanation: Gestational diabetes is a non-communicable disease. The article contributes to this target by focusing on prevention and early diagnosis. It identifies key risk factors like high pre-pregnancy body weight and BMI, and develops a prediction model for early GDM (eGDM). This research helps in identifying at-risk populations for targeted prevention and earlier treatment, which is crucial for managing NCDs.
• Target 3.1: By 2030, reduce the global maternal mortality ratio to less than 70 per 100,000 live births.
  • Explanation: While the article does not directly measure maternal mortality, GDM is a major contributor to maternal morbidity and can lead to complications during pregnancy and childbirth. By improving the understanding and diagnosis of GDM, specifically by differentiating between early and standard GDM, the research supports better management of pregnancies. This improved clinical management helps prevent adverse maternal health outcomes, thus contributing to the broader goal of reducing maternal mortality and morbidity.

3. Are there any indicators mentioned or implied in the article that can be used to measure progress towards the identified targets?

• Prevalence of GDM: The article provides specific data on the frequency of GDM diagnosis, classifying 1,639 patients as early GDM (eGDM) and 16,856 as standard GDM (sGDM). Tracking the prevalence of GDM, particularly eGDM, serves as an indicator of the burden of this NCD among pregnant women.
• Prevalence of obesity as a risk factor: The article explicitly identifies high pre-pregnancy body weight and BMI as major risk factors. It states, “Women with eGDM were more likely to present with a higher pre-pregnancy body weight (86.65 kg vs. 76.81 kg) and higher pre-pregnancy BMI (31.15 vs. 27.99 kg/m²).” The prevalence of obesity (e.g., BMI > 40 kg/m²) in pregnant women is a direct indicator for measuring NCD risk.
• Biomarkers for screening and diagnosis: The article suggests specific clinical measurements that can be used as indicators for early detection.
  • Fasting Plasma Glucose (FPG): The study found that “fasting glucose emerged as one of the strongest predictive values for eGDM manifestation” and suggests that a level of “98 mg/dl [5,4 mmol/L] could serve as an effective screening tool for eGDM.” This value can be used as a performance indicator for screening programs.
  • HbA1c levels: The article notes that an eGDM diagnosis was associated with a higher HbA1c of 5.22%, which can be used as a monitoring indicator.
• History of GDM in previous pregnancies: The research identifies a previous history of GDM as one of the “strongest associations with eGDM.” The proportion of women with a history of GDM who develop it again (28.78% in the study) is a key indicator for identifying high-risk groups for targeted maternal health interventions.

Summary Table

4. Create a table with three columns titled ‘SDGs, Targets and Indicators” to present the findings from analyzing the article.

Source: dmsjournal.biomedcentral.com