Automated Machine Learning (AutoML)-Derived Preconception Predictive Risk Model to Guide Early Intervention for Gestational Diabetes Mellitus

The increasing prevalence of gestational diabetes mellitus (GDM) is contributing to the rising global burden of type 2 diabetes (T2D) and intergenerational cycle of chronic metabolic disorders. Primary lifestyle interventions to manage GDM, including second trimester dietary and exercise guidance, have met with limited success due to late implementation, poor adherence and generic guidelines. In this study, we aimed to build a preconception-based GDM predictor to enable early intervention. We also assessed the associations of top predictors with GDM and adverse birth outcomes. Our evolutionary algorithm-based automated machine learning (AutoML) model was implemented with data from 222 Asian multi-ethnic women in a preconception cohort study, Singapore Preconception Study of Long-Term Maternal and Child Outcomes (S-PRESTO). A stacked ensemble model with a gradient boosting classifier and linear support vector machine classifier (stochastic gradient descent training) was derived using genetic programming, achieving an excellent AUC of 0.93 based on four features (glycated hemoglobin A(1c) (HbA(1c)), mean arterial blood pressure, fasting insulin, triglycerides/HDL ratio). The results of multivariate logistic regression model showed that each 1 mmol/mol increase in preconception HbA(1c) was positively associated with increased risks of GDM (p = 0.001, odds ratio (95% CI) 1.34 (1.13-1.60)) and preterm birth (p = 0.011, odds ratio 1.63 (1.12-2.38)). Optimal control of preconception HbA(1c) may aid in preventing GDM and reducing the incidence of preterm birth. Our trained predictor has been deployed as a web application that can be easily employed in GDM intervention programs, prior to conception.Peer reviewe

The Association between Mushroom Consumption and Mild Cognitive Impairment: A Community-Based Cross-Sectional Study in Singapore

10.3233/JAD-180959JOURNAL OF ALZHEIMERS DISEASE681197-20

Automated Machine Learning (AutoML)-Derived Preconception Predictive Risk Model to Guide Early Intervention for Gestational Diabetes Mellitus

10.3390/ijerph19116792International Journal of Environmental Research and Public Health191

Automated Machine Learning (AutoML)-Derived Preconception Predictive Risk Model to Guide Early Intervention for Gestational Diabetes Mellitus

10.3390/ijerph19116792International Journal of Environmental Research and Public Health191

Automated Machine Learning (AutoML)-Derived Preconception Predictive Risk Model to Guide Early Intervention for Gestational Diabetes Mellitus

The increasing prevalence of gestational diabetes mellitus (GDM) is contributing to the rising global burden of type 2 diabetes (T2D) and intergenerational cycle of chronic metabolic disorders. Primary lifestyle interventions to manage GDM, including second trimester dietary and exercise guidance, have met with limited success due to late implementation, poor adherence and generic guidelines. In this study, we aimed to build a preconception-based GDM predictor to enable early intervention. We also assessed the associations of top predictors with GDM and adverse birth outcomes. Our evolutionary algorithm-based automated machine learning (AutoML) model was implemented with data from 222 Asian multi-ethnic women in a preconception cohort study, Singapore Preconception Study of Long-Term Maternal and Child Outcomes (S-PRESTO). A stacked ensemble model with a gradient boosting classifier and linear support vector machine classifier (stochastic gradient descent training) was derived using genetic programming, achieving an excellent AUC of 0.93 based on four features (glycated hemoglobin A1c (HbA1c), mean arterial blood pressure, fasting insulin, triglycerides/HDL ratio). The results of multivariate logistic regression model showed that each 1 mmol/mol increase in preconception HbA1c was positively associated with increased risks of GDM (p = 0.001, odds ratio (95% CI) 1.34 (1.13–1.60)) and preterm birth (p = 0.011, odds ratio 1.63 (1.12–2.38)). Optimal control of preconception HbA1c may aid in preventing GDM and reducing the incidence of preterm birth. Our trained predictor has been deployed as a web application that can be easily employed in GDM intervention programs, prior to conception

Safety, Tolerability, and Pharmacokinetics of beta-Cryptoxanthin Supplementation in Healthy Women : A Double-Blind, Randomized, Placebo-Controlled Clinical Trial

Peer reviewe

Safety, Tolerability, and Pharmacokinetics of β-Cryptoxanthin Supplementation in Healthy Women: A Double-Blind, Randomized, Placebo-Controlled Clinical Trial

Background: β-cryptoxanthin is a dietary carotenoid for which there have been few studies on the safety and pharmacokinetics following daily oral supplementation. Methods: 90 healthy Asian women between 21 and 35 years were randomized into three groups: 3 and 6 mg/day oral β-cryptoxanthin, and placebo. At 2, 4, and 8 weeks of supplementation, plasma carotenoid levels were measured. The effects of β-cryptoxanthin on blood retinoid-dependent gene expression, mood, physical activity and sleep, metabolic parameters, and fecal microbial composition were investigated. Results: β-cryptoxanthin supplementation for 8 weeks (3 and 6 mg/day) was found to be safe and well tolerated. Plasma β-cryptoxanthin concentration was significantly higher in the 6 mg/day group (9.0 ± 4.1 µmol/L) compared to 3 mg/day group (6.0 ± 2.6 µmol/L) (p < 0.03), and placebo (0.4 ± 0.1 µmol/L) (p < 0.001) after 8 weeks. Plasma all-trans retinol, α-cryptoxanthin, α-carotene, β-carotene, lycopene, lutein, and zeaxanthin levels were not significantly changed. No effects were found on blood retinol-dependent gene expression, mood, physical activity and sleep, metabolic parameters, and fecal microbial composition. Conclusions: Oral β-cryptoxanthin supplementation over 8 weeks lead to high plasma concentrations of β-cryptoxanthin, with no impact on other carotenoids, and was well tolerated in healthy women

Effect of an Asian-adapted Mediterranean diet and pentadecanoic acid on fatty liver disease : the TANGO randomized controlled trial

Background: Weight loss is the most effective treatment for nonalcoholic fatty liver disease (NAFLD). There is evidence that the Mediterranean diets rich in unsaturated fatty acids and fiber have beneficial effects on weight homeostasis and metabolic risk factors in individuals with NAFLD. Studies have also shown that higher circulating concentrations of pentadecanoic acid (C15:0) are associated with a lower risk for NAFLD. Objectives: To examine the effects of a Mediterranean-like, culturally contextualized Asian diet rich in fiber and unsaturated fatty acids, with or without C15:0 supplementation, in Chinese females with NAFLD. Methods: In a double-blinded, parallel-design, randomized controlled trial, 88 Chinese females with NAFLD were randomly assigned to 1 of the 3 groups for 12 wk: diet with C15:0 supplementation (n = 31), diet without C15:0 supplementation (n = 28), or control (habitual diet and no C15:0 supplementation, n = 29). At baseline and after the intervention, body fat percentage, intrahepatic lipid content, muscle and abdominal fat, liver enzymes, cardiometabolic risk factors, and gut microbiome were assessed. Results: In the intention-to-treat analysis, weight reductions of 4.0 ± 0.5 kg (5.3%), 3.4 ± 0.5 kg (4.5%), and 1.5 ± 0.5 kg (2.1%) were achieved in the diet-with-C15:0, diet without-C15:0, and the control groups, respectively. The proton density fat fraction (PDFF) of the liver decreased by 33%, 30%, and 10%, respectively. Both diet groups achieved significantly greater reductions in body weight, liver PDFF, total cholesterol, gamma-glutamyl transferase, and triglyceride concentrations compared with the control group. C15:0 supplementation reduced LDL-cholesterol further, and increased the abundance of Bifidobacterium adolescentis. Fat mass, visceral adipose tissue, subcutaneous abdominal adipose tissue (deep and superficial), insulin, glycated hemoglobin, and blood pressure decreased significantly in all groups, in parallel with weight loss. Conclusion: Mild weight loss induced by a Mediterranean-like diet adapted for Asians has multiple beneficial health effects in females with NAFLD. C15:0 supplementation lowers LDL-cholesterol and may cause beneficial shifts in the gut microbiome. Trial registration number: This trial was registered at the clinicaltrials.gov as NCT05259475.Peer reviewe