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

Environmental filtering controls soil biodiversity in wet tropical ecosystems

9 páginas..- 4 figuras.- referencias.- Supplementary data to this article can be found online at https://doi. org/10.1016/j.soilbio.2022.108571The environmental factors controlling soil biodiversity along resource gradients remain poorly understood in wet tropical ecosystems. Aboveground biodiversity is expected to be driven by changes in nutrient availability in these ecosystems, however, much less is known about the importance of nutrient availability in driving soil biodiversity. Here, we combined a cross-continental soil survey across tropical regions with a three decades' field experiment adding nitrogen (N) and phosphorus (P) (100 kg N ha(-1)y(-1) and 100 kg P ha(-1)y(-1)) to Hawai'ian tropical forests with contrasting substrate ages (300 and 4,100,000 years) to investigate the influence of nutrient availability to explain the biodiversity of soil bacteria, fungi, protists, invertebrates and key functional genes. We found that soil biodiversity was driven by soil acidification during long-term pedogenesis and across environmental gradients, rather than by nutrient limitations. In fact, our results showed that experimental N additions caused substantial acidification in soils from Hawai'i. These declines in pH were related to large decreases in soil biodiversity from tropical ecosystems in four continents. Moreover, the microbial activity did not change in response to long-term N and P additions. We concluded that environmental filtering drives the biodiversity of multiple soil organisms, and that the acidification effects associated with N additions can further create substantial undesired net negative effects on overall soil biodiversity in naturally tropical acid soils. This knowledge is integral for the understanding and management of soil biodiversity in tropical ecosystems globally.Supported by a Ramón y Cajal grant (RYC2018-025483-I), a “Ayuda P.P. 2020. Desarrollo Lineas Investigación Propias (UPO), a project from the Spanish Ministry of Science and Innovation (PID2020-115813RA-I00), and a project PAIDI 2020 from the Junta de Andalucía (P20_00879). H.Y.C. is supported by National Natural Science Foundation of China (32101335), China Postdoctoral Science Foundation (2021M690589), Innovation Project of Young Technological Talents in Changchun City (21QC07), and Fundamental Research Funds for the Central Universities (2412021QD014). J.P.V. is thankful to DST and SERB (Science and Engineering Research Board), India for financial support for plant-microbe interaction research. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.Peer reviewe

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

A cohort survey of the effect of an educational intervention on human papillomavirus vaccine-related knowledge and attitudes among pre-university female students in Singapore

Introduction: Although cervical cancer is the 10th most common cancer among females in Singapore, it is a vaccine-preventable cancer. Human papillomavirus vaccines combined with regular Pap (papillomavirus) smears have been shown to reduce the mortality and morbidity associated with cervical cancer. We assessed the knowledge and attitudes towards cervical cancer, human papillomavirus and its vaccination amongst pre-university female students and assessed if a short educational presentation can improve this knowledge and acceptability of the vaccine. Methods: A cohort survey was carried out amongst 150 female students at Hwa Chong Junior College via questionnaires administered anonymously. Standardized educational presentations were carried out followed by post intervention questionnaires. Results: Almost all the students were aged between 15 and 19 years old (99.3%). At baseline, only 29% and 27% of the students had ever heard of human papillomavirus and its vaccines respectively. The most common source of information cited was from a family member or a friend (22.5%). Only 4% had been administered the human papillomavirus vaccine. Mean knowledge scores significantly improved from 4.95 to 8.61 out of 10 after the intervention. Of those who did not have the vaccine, only 31.3% were willing to consider the human papillomavirus vaccine pre-intervention, with a statistically significant improvement to 51.4% post intervention. Conclusion: Low awareness about human papillomavirus, its relationship to cervical cancer and its prevention is common, even amongst well-educated students. Most of them obtained the information from family and/or friends. A short educational presentation was able to improve the knowledge scores, and improve the acceptability of the human papillomavirus vaccine

Environmental filtering controls soil biodiversity in wet tropical ecosystems

Acknowledgements We are grateful to Dr. Minna Zhang and Dr. Yinong Li from Northeast Normal University, Dr. Xincheng Li from Fudan University, and Dr. Shengen Liu from China Three Gorges University for the valuable feedback and suggestions for the data analysis in the earlier version. M.D-B. is supported by a Ramón y Cajal grant (RYC2018-025483-I), a “Ayuda P.P. 2020. Desarrollo Lineas Investigación Propias (UPO), a project from the Spanish Ministry of Science and Innovation (PID2020-115813RA-I00), and a project PAIDI 2020 from the Junta de Andalucía (P20_00879). H.Y.C. is supported by National Natural Science Foundation of China (32101335), China Postdoctoral Science Foundation (2021M690589), Innovation Project of Young Technological Talents in Changchun City (21QC07), and Fundamental Research Funds for the Central Universities (2412021QD014). J.P.V. is thankful to DST and SERB (Science and Engineering Research Board), India for financial support for plant-microbe interaction research. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.The environmental factors controlling soil biodiversity along resource gradients remain poorly understood in wet tropical ecosystems. Aboveground biodiversity is expected to be driven by changes in nutrient availability in these ecosystems, however, much less is known about the importance of nutrient availability in driving soil biodiversity. Here, we combined a cross-continental soil survey across tropical regions with a three decades' field experiment adding nitrogen (N) and phosphorus (P) (100 kg N ha−1y−1 and 100 kg P ha−1y−1) to Hawai'ian tropical forests with contrasting substrate ages (300 and 4,100,000 years) to investigate the influence of nutrient availability to explain the biodiversity of soil bacteria, fungi, protists, invertebrates and key functional genes. We found that soil biodiversity was driven by soil acidification during long-term pedogenesis and across environmental gradients, rather than by nutrient limitations. In fact, our results showed that experimental N additions caused substantial acidification in soils from Hawai'i. These declines in pH were related to large decreases in soil biodiversity from tropical ecosystems in four continents. Moreover, the microbial activity did not change in response to long-term N and P additions. We concluded that environmental filtering drives the biodiversity of multiple soil organisms, and that the acidification effects associated with N additions can further create substantial undesired net negative effects on overall soil biodiversity in naturally tropical acid soils. This knowledge is integral for the understanding and management of soil biodiversity in tropical ecosystems globally.Depto. de Biodiversidad, Ecología y EvoluciónFac. de Ciencias BiológicasTRUEpu

The influence of soil age on ecosystem structure and function across biomes.

The importance of soil age as an ecosystem driver across biomes remains largely unresolved. By combining a cross-biome global field survey, including data for 32 soil, plant, and microbial properties in 16 soil chronosequences, with a global meta-analysis, we show that soil age is a significant ecosystem driver, but only accounts for a relatively small proportion of the cross-biome variation in multiple ecosystem properties. Parent material, climate, vegetation and topography predict, collectively, 24 times more variation in ecosystem properties than soil age alone. Soil age is an important local-scale ecosystem driver; however, environmental context, rather than soil age, determines the rates and trajectories of ecosystem development in structure and function across biomes. Our work provides insights into the natural history of terrestrial ecosystems. We propose that, regardless of soil age, changes in the environmental context, such as those associated with global climatic and land-use changes, will have important long-term impacts on the structure and function of terrestrial ecosystems across biomes

The influence of soil age on ecosystem structure and function across biomes

The importance of soil age as an ecosystem driver across biomes remains largely unresolved. By combining a cross-biome global field survey, including data for 32 soil, plant, and microbial properties in 16 soil chronosequences, with a global meta-analysis, we show that soil age is a significant ecosystem driver, but only accounts for a relatively small proportion of the cross-biome variation in multiple ecosystem properties. Parent material, climate, vegetation and topography predict, collectively, 24 times more variation in ecosystem properties than soil age alone. Soil age is an important local-scale ecosystem driver; however, environmental context, rather than soil age, determines the rates and trajectories of ecosystem development in structure and function across biomes. Our work provides insights into the natural history of terrestrial ecosystems. We propose that, regardless of soil age, changes in the environmental context, such as those associated with global climatic and land-use changes, will have important long-term impacts on the structure and function of terrestrial ecosystems across biomes