Improving Machine Learning Diabetes Prediction Models for the Utmost Clinical Effectiveness

The early prediction of diabetes can facilitate interventions to prevent or delay it. This study proposes a diabetes prediction model based on machine learning (ML) to encourage individuals at risk of diabetes to employ healthy interventions. A total of 38,379 subjects were included. We trained the model on 80% of the subjects and verified its predictive performance on the remaining 20%. Furthermore, the performances of several algorithms were compared, including logistic regression, decision tree, random forest, eXtreme Gradient Boosting (XGBoost), Cox regression, and XGBoost Survival Embedding (XGBSE). The area under the receiver operating characteristic curve (AUROC) of the XGBoost model was the largest, followed by those of the decision tree, logistic regression, and random forest models. For the survival analysis, XGBSE yielded an AUROC exceeding 0.9 for the 2- to 9-year predictions and a C-index of 0.934, while the Cox regression achieved a C-index of 0.921. After lowering the threshold from 0.5 to 0.25, the sensitivity increased from 0.011 to 0.236 for the 2-year prediction model and from 0.607 to 0.994 for the 9-year prediction model, while the specificity showed negligible changes. We developed a high-performance diabetes prediction model that applied the XGBSE algorithm with threshold adjustment. We plan to use this prediction model in real clinical practice for diabetes prevention after simplifying and validating it externally

Prediction of Cardiovascular Complication in Patients with Newly Diagnosed Type 2 Diabetes Using an XGBoost/GRU-ODE-Bayes-Based Machine-Learning Algorithm

Background Cardiovascular disease is life-threatening yet preventable for patients with type 2 diabetes mellitus (T2DM). Because each patient with T2DM has a different risk of developing cardiovascular complications, the accurate stratification of cardiovascular risk is critical. In this study, we proposed cardiovascular risk engines based on machine-learning algorithms for newly diagnosed T2DM patients in Korea. Methods To develop the machine-learning-based cardiovascular disease engines, we retrospectively analyzed 26,166 newly diagnosed T2DM patients who visited Seoul St. Mary’s Hospital between July 2009 and April 2019. To accurately measure diabetes-related cardiovascular events, we designed a buffer (1 year), an observation (1 year), and an outcome period (5 years). The entire dataset was split into training and testing sets in an 8:2 ratio, and this procedure was repeated 100 times. The area under the receiver operating characteristic curve (AUROC) was calculated by 10-fold cross-validation on the training dataset. Results The machine-learning-based risk engines (AUROC XGBoost=0.781±0.014 and AUROC gated recurrent unit [GRU]-ordinary differential equation [ODE]-Bayes=0.812±0.016) outperformed the conventional regression-based model (AUROC=0.723± 0.036). Conclusion GRU-ODE-Bayes-based cardiovascular risk engine is highly accurate, easily applicable, and can provide valuable information for the individualized treatment of Korean patients with newly diagnosed T2DM

Risk of developing chronic kidney disease in young-onset Type 2 diabetes in Korea

Abstract We investigated the risk of developing chronic kidney disease (CKD) in patients with young-onset Type 2 diabetes (YOD, diagnosed age < 40 years). We enrolled 84,384 patients aged 20–64 who started anti-diabetic medication between 2010 and 2011 from the Korea National Health Insurance Sharing Service; patients with Type 1 diabetes or a history of CKD were excluded. Multivariate logistic regression analyses were performed to adjust for YOD-distinct variables and compare the incidence of CKD between YOD and late-onset diabetes (LOD, diagnosed age ≥ 40 years). During the median observation period of 5.16 years (interquartile range: 4.58–5.77 years), 1480 out of 77,039 LOD patients and 34 out of 7345 YOD patients developed CKD. Patients with YOD had distinct baseline characteristics compared with the patients with LOD. The odds ratio of developing CKD in patients with YOD over LOD was 1.70 (95% CI 1.15–2.51) after adjusting clinically distinct variables. The increased CKD odds in YOD compared with LOD was greater in the non-smoking group (OR 2.03, 95% CI 1.26–3.26) than in the smoking group (OR 1.49, 95% CI 0.74–2.98, p = 0.0393 for interaction). Among YOD patients, hypertension (34.76% vs. 64.71%, p = 0.0003), dyslipidemia (46.87% vs. 73.53%, p = 0.0019), and sulfonylurea use (35.54% vs. 52.94%, p = 0.0345) were associated with CKD development. YOD patients have a greater risk of developing CKD than LOD patients after adjusting clinically distinct variables

Multiparity increases the risk of diabetes by impairing the proliferative capacity of pancreatic β cells

Abstract Pregnancy imposes a substantial metabolic burden on women, but little is known about whether or how multiple pregnancies increase the risk of maternal postpartum diabetes. In this study, we assessed the metabolic impact of multiple pregnancies in humans and in a rodent model. Mice that underwent multiple pregnancies had increased adiposity, but their glucose tolerance was initially improved compared to those of age-matched virgin mice. Later, however, insulin resistance developed over time, but insulin secretory function and compensatory pancreatic β cell proliferation were impaired in multiparous mice. The β cells of multiparous mice exhibited aging features, including telomere shortening and increased expression of Cdkn2a. Single-cell RNA-seq analysis revealed that the β cells of multiparous mice exhibited upregulation of stress-related pathways and downregulation of cellular respiration- and oxidative phosphorylation-related pathways. In humans, women who delivered more than three times were more obese, and their plasma glucose concentrations were elevated compared to women who had delivered three or fewer times, as assessed at 2 months postpartum. The disposition index, which is a measure of the insulin secretory function of β cells, decreased when women with higher parity gained body weight after delivery. Taken together, our findings indicate that multiple pregnancies induce cellular stress and aging features in β cells, which impair their proliferative capacity to compensate for insulin resistance